U.S. patent application number 17/530040 was filed with the patent office on 2022-05-19 for method and apparatus for minimally intrusive instruction pointer-aware processing resource activity profiling.
This patent application is currently assigned to Intel Corporation. The applicant listed for this patent is Intel Corporation. Invention is credited to Michael Cole, Alexandr Kurylev, Subramaniam Maiyuran, Piotr Reiter, Sriharsha Vadlamani, Vikranth Vemulapalli.
Application Number | 20220156068 17/530040 |
Document ID | / |
Family ID | |
Filed Date | 2022-05-19 |
United States Patent
Application |
20220156068 |
Kind Code |
A1 |
Cole; Michael ; et
al. |
May 19, 2022 |
METHOD AND APPARATUS FOR MINIMALLY INTRUSIVE INSTRUCTION
POINTER-AWARE PROCESSING RESOURCE ACTIVITY PROFILING
Abstract
Systems and methods for minimally intrusive instruction
pointer-aware processing resource activity profiling are disclosed.
In one embodiment, a graphics processor includes a grouping of
processing resources and control logic that is associated with the
grouping of processing resources. The control logic is configured
to sample a state of at least one processing resource of the
grouping of processing resources and to determine activity data
from the state with the activity data including at least one of
stalls and reason counts for stalling activity, instruction types,
pipeline utilization, thread utilization, and shader activity.
Inventors: |
Cole; Michael; (Folsom,
CA) ; Kurylev; Alexandr; (Nizhny Novgorod, RU)
; Maiyuran; Subramaniam; (Gold River, CA) ;
Vemulapalli; Vikranth; (Folsom, CA) ; Vadlamani;
Sriharsha; (Folsom, CA) ; Reiter; Piotr;
(Gdansk, PL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Intel Corporation |
Santa Clara |
CA |
US |
|
|
Assignee: |
Intel Corporation
Santa Clara
CA
|
Appl. No.: |
17/530040 |
Filed: |
November 18, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16585427 |
Sep 27, 2019 |
11210094 |
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17530040 |
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International
Class: |
G06F 9/30 20060101
G06F009/30; G06F 9/38 20060101 G06F009/38; G06F 12/0815 20060101
G06F012/0815; G06F 9/50 20060101 G06F009/50 |
Claims
1. A graphics processor, comprising: a grouping of processing
resources; and control logic that is associated with the grouping
of processing resources, the control logic is configured to sample
a state of at least one processing resource of the grouping of
processing resources and to determine activity data from the state
with the activity data including reason counts for counting
occurrences for stalling activity, or activity counts for
instruction types, pipeline utilization, thread utilization, or
shader activity.
2. The graphics processor of claim 1, further comprising: a cache
unit that is associated with the grouping of processing resources,
the cache unit to receive an instruction pointer address and the
activity data including a stall reason for each state of processing
resources that are associated with the cache unit.
3. The graphics processor of claim 2, wherein each sampling of a
state is scheduled for a chosen clock cycle and is minimally
intrusive.
4. The graphics processor of claim 1, wherein the control logic is
configured to store a state when threads are allocated on a
processing resource with no instruction being executed for a chosen
cycle that is sampled.
5. The graphics processor of claim 4, wherein the control logic is
configured to discard a state for a chosen cycle that is sampled if
the processing resource is idle or executing an instruction.
6. The graphics processor of claim 1, wherein the control logic is
configured to interleave samplings of states of processing
resources among the grouping of processing resources and other
groupings of processing resources, to resolve the states into one
of a number of supported stall reasons, and to prioritize the
supported stall reasons based on a priority level of the stall
reasons.
7. The graphics processor of claim 1, wherein the supported stalls
and reason counts for stalling activity comprise a synch stall
field for a stall or delay between threads to reach a common point,
an instruction fetch field for an instruction fetch from memory
that is stalled, a scoreboard field for a stall based on a data
dependency, a send stall field for a send bus bandwidth limit for
an processing resource, a pipe stall field for a stall within a
pipeline, and an internal stall field for a stall caused from a
memory bank collision.
8. A cache structure, comprising: logic to perform operations of
the cache structure; and memory coupled to the logic, the memory to
store instruction pointer addresses and associated data fields to
indicate activity data from sampling of processing resources,
wherein the logic is configured to receive an instruction pointer
address and activity data for a state of processing resources that
are associated with the cache structure.
9. The cache structure of claim 8, wherein the logic is configured
to perform an instruction pointer address lookup within the cache
structure.
10. The cache structure of claim 9, wherein the logic is configured
to build an entry for a new cache line when the instruction pointer
lookup misses, to store the instruction pointer address and the
activity data in the new cache line, to initialize the identified
activity including a stall reason to a count while all other reason
counts are initialized to a different count.
11. The cache structure of claim 10, wherein the logic is
configured to determine if all available lines of the cache
structure are occupied and to perform a capacity-eviction to evict
an existing line if all available lines of the cache structure are
occupied.
12. The cache structure of claim 9, wherein the logic is configured
to determine a hit for instruction pointer address lookup, to
perform a read operation of a cache line for the instruction
pointer address, to perform a modify operation to increment a count
of the identified activity, and to perform a write operation for
the cache line.
13. The cache structure of claim 9, wherein the logic is configured
for a maximum value eviction when a given cache line has an
activity count that reaches a maximum representable value and
performs the maximum value eviction by evicting the instruction
pointer address and its corresponding data to a circular buffer in
main memory.
14. A method for minimally intrusive profiling of a graphics
processing unit (GPU), comprising: receiving, with a cache unit, an
instruction pointer address and activity data for each state of
processing resources that are associated with the cache unit; and
performing an instruction pointer address lookup within the cache
unit for the received instruction pointer address and associated
activity data.
15. The method of claim 14, further comprising: building an entry
for a new cache line when the instruction pointer lookup
misses.
16. The method of claim 15, further comprising: storing the
instruction pointer address and the activity data in the new cache
line; and initializing the identified activity including a stall
reason to 1 while all other reason counts are initialized to 0.
17. The method of claim 16, further comprising: determining if all
available lines of the cache structure are occupied and to perform
a capacity-eviction to evict an existing line if all available
lines of the cache structure are occupied.
18. The method of claim 15, further comprising: determining a hit
for instruction pointer address lookup.
19. The method of claim 18, further comprising: performing a read
operation of a cache line for the instruction pointer address;
performing a modify operation to increment a count of the
identified activity for the instruction pointer address; and
performing a write operation for the cache line.
20. The method of claim 19, further comprising: performing a
maximum value eviction when a given cache line has an activity
count that reaches a maximum representable value, wherein
performing the maximum value eviction comprises evicting the
instruction pointer address and its corresponding data to a
circular buffer in main memory.
21. (canceled)
Description
CROSS-REFERENCE
[0001] This application is a continuation of U.S. application Ser.
No. 16/585,427, filed Sep. 27, 2019, the entire contents of which
are hereby incorporated herein by reference.
FIELD
[0002] Embodiments relate generally to data processing and more
particularly to minimally intrusive instruction pointer-aware
processing resource activity profiling via an apparatus (e.g., a
general-purpose graphics processing unit).
BACKGROUND OF THE DESCRIPTION
[0003] Current parallel graphics data processing includes systems
and methods developed to perform specific operations on graphics
data such as, for example, linear interpolation, tessellation,
rasterization, texture mapping, depth testing, etc. Traditionally,
graphics processors used fixed function computational units to
process graphics data; however, more recently, portions of graphics
processors have been made programmable, enabling such processors to
support a wider variety of operations for processing vertex and
fragment data.
[0004] To further increase performance, graphics processors
typically implement processing techniques such as pipelining that
attempt to process, in parallel, as much graphics data as possible
throughout the different parts of the graphics pipeline. Parallel
graphics processors with single instruction, multiple thread (SIMT)
architectures are designed to maximize the amount of parallel
processing in the graphics pipeline. In an SIMT architecture,
groups of parallel threads attempt to execute program instructions
synchronously together as often as possible to increase processing
efficiency. A general overview of software and hardware for SIMT
architectures can be found in Shane Cook, CUDA Programming Chapter
3, pages 37-51 (2013).
BRIEF DESCRIPTION OF THE DRAWINGS
[0005] So that the manner in which the above recited features of
the present embodiments can be understood in detail, a more
particular description of the embodiments, briefly summarized
above, may be had by reference to embodiments, son of which are
illustrated in the appended drawings. It is to be noted, however,
that the appended drawings illustrate only typical embodiments and
are therefore not to be considered limiting of its scope.
[0006] FIG. 1 is a block diagram of a processing system 100,
according to an embodiment;
[0007] FIG. 2A-2D illustrate computing systems and graphics
processors, according to embodiments;
[0008] FIG. 3A-3C are block diagrams of additional graphics
processor and compute accelerator architectures, according to
embodiments;
[0009] FIG. 4 is a block diagram of a graphics processing engine
410 of a graphics processor in accordance with some
embodiments;
[0010] FIGS. 5A-5B illustrate thread execution logic 500 including
an array of processing elements employed in a graphics processor
core according to embodiments;
[0011] FIG. 6 illustrates an additional execution unit 600,
according to an embodiment;
[0012] FIG. 7 is a block diagram illustrating a graphics processor
instruction formats 700 according to some embodiments;
[0013] FIG. 8 is a block diagram of another embodiment of a
graphics processor 800, according to an embodiment;
[0014] FIG. 9A is a block diagram illustrating a graphics processor
command format 900 according to some embodiments;
[0015] FIG. 9B is a block diagram illustrating a graphics processor
command sequence 910 according to an embodiment.
[0016] FIG. 10 illustrates an exemplary graphics software
architecture for a data processing system 1000 according to some
embodiments;
[0017] FIG. 11A is a block diagram illustrating an IP core
development system 1100 that may be used to manufacture an
integrated circuit to perform operations according to an
embodiment;
[0018] FIG. 11B illustrates a cross-section side view of an
integrated circuit package assembly 1170, according to some
embodiments;
[0019] FIG. 11C illustrates a package assembly 1190 that includes
multiple units of hardware logic chiplets connected to a substrate
1180 (e.g., base die).
[0020] FIG. 11D illustrates a package assembly 1194 including
interchangeable chiplets 1195, according to an embodiment.
[0021] FIG. 12 illustrates an exemplary integrated circuit and
FIGS. 13A-13B illustrate associated graphics processors that may be
fabricated using one or more IP cores, according to various
embodiments described herein.
[0022] FIG. 14 shows an example execution circuitry 1400 for
groupings of EUs in accordance with one embodiment.
[0023] FIG. 15 provides an illustration of execution circuitry and
EU stall reason resolution in accordance with one embodiment.
[0024] FIG. 16 illustrates a method 1600 having an exemplary
sequence of operations for processing a new sample in a cache-like
aggregation structure (e.g., cache units 1450A-1450H) in accordance
with one embodiment.
[0025] FIG. 17 illustrates an example table of the cache-like
aggregation structure with sample data and stall reasons in
accordance with one embodiment.
[0026] FIG. 18 shows an example table of contents of a main-memory
circular buffer in accordance with one embodiment.
[0027] FIG. 19A-19C illustrate additional graphics multiprocessors,
according to embodiments.
DETAILED DESCRIPTION
[0028] In some embodiments, a graphics processing unit (GPU) is
communicatively coupled to host/processor cores to accelerate
graphics operations, machine-learning operations, pattern analysis
operations, and various general-purpose GPU (GPGPU) functions. The
GPU may be communicatively coupled to the host processor/cores over
a bus or another interconnect (e.g., a high-speed interconnect such
as PCIe or NVLink). In other embodiments, the GPU may be integrated
on the same package or chip as the cores and communicatively
coupled to the cores over an internal processor bus/interconnect
(i.e., internal to the package or chip). Regardless of the manner
in which the GPU is connected, the processor cores may allocate
work to the GPU in the form of sequences of commands/instructions
contained in a work descriptor. The GPU then uses dedicated
circuitry/logic for efficiently processing these
commands/instructions.
[0029] In the following description, numerous specific details are
set forth to provide a more thorough understanding. However, it ill
be apparent to one of skill in the art that the embodiments
described herein may be practiced without one or more of these
specific details. In other instances, well-known features have not
been described to avoid obscuring the details of the present
embodiments.
System Overview
[0030] FIG. 1 is a block diagram of a processing system 100,
according to an embodiment. System 100 may be used in a single
processor desktop system, a multiprocessor workstation system, or a
server system having a large number of processors 102 or processor
cores 107. In one embodiment, the system 100 is a processing
platform incorporated within a system-on-a-chip (SoC) integrated
circuit for use in mobile, handheld, or embedded devices such as
within Internet-of-things (IoT) devices with wired or wireless
connectivity to a local or wide area network.
[0031] In one embodiment, system 100 can include, couple with, or
be integrated within: a server-based gaming platform; a game
console, including a game and media console; a mobile gaming
console, a handheld game console, or an online game console. In
some embodiments the system 100 is part of a mobile phone, smart
phone, tablet computing device or mobile Internet-connected device
such as a laptop with low internal storage capacity. Processing
system 100 can also include, couple with, or be integrated within:
a wearable device, such as a smart watch wearable device; smart
eyewear or clothing enhanced with augmented reality (AR) or virtual
reality (VR) features to provide visual, audio or tactile outputs
to supplement real world visual, audio or tactile experiences or
otherwise provide text, audio, graphics, video, holographic images
or video, or tactile feedback; other augmented reality (AR) device;
or other virtual reality (VR) device. In some embodiments, the
processing system 100 includes or is part of a television or set
top box device. In one embodiment, system 100 can include, couple
with, or be integrated within a self-driving vehicle such as a bus,
tractor trailer, car, motor or electric power cycle, plane or
glider (or any combination thereof). The self-driving vehicle may
use system 100 to process the environment sensed around the
vehicle.
[0032] In some embodiments, the one or more processors 102 each
include one or more processor cores 107 to process instructions
which, when executed, perform operations for system or user
software. In some embodiments, at least one of the one or more
processor cores 107 is configured to process a specific instruction
set 109. In some embodiments, instruction set 109 may facilitate
Complex Instruction Set Computing (CISC), Reduced Instruction Set
Computing (RISC), or computing via a Very Long Instruction Word
(VLIW). One or more processor cores 107 may process a different
instruction set 109, which may include instructions to facilitate
the emulation of other instruction sets. Processor core 107 may
also include other processing devices, such as a Digital Signal
Processor (DSP).
[0033] In some embodiments, the processor 102 includes cache memory
104. Depending on the architecture, the processor 102 can have a
single internal cache or multiple levels of internal cache. In some
embodiments, the cache memory is shared among various components of
the processor 102. In some embodiments, the processor 102 also uses
an external cache (e.g., a Level-3 (L3) cache or Last Level Cache
(LLC)) (not shown), which may be shared among processor cores 107
using known cache coherency techniques. A register file 106 can be
additionally included in processor 102 and may include different
types of registers for storing different types of data (e.g.,
integer registers, floating point registers, status registers, and
an instruction pointer register). Some registers may be
general-purpose registers, while other registers may be specific to
the design of the processor 102.
[0034] In some embodiments, one or more processor(s) 102 are
coupled with one or more interface bus(es) 110 to transmit
communication signals such as address, data, or control signals
between processor 102 and other components in the system 100. The
interface bus 110, in one embodiment, can be a processor bus, such
as a version of the Direct Media Interface (DMI) bus. However,
processor busses are not limited to the DMI bus, and may include
one or more Peripheral Component Interconnect buses (e.g., PCI, PCI
express), memory busses, or other types of interface busses. In one
embodiment the processor(s) 102 include an integrated memory
controller 116 and a platform controller hub 130. The memory
controller 116 facilitates communication between a memory device
and other components of the system 100, while the platform
controller hub (PCH) 130 provides connections to I/O devices via a
local I/O bus.
[0035] The memory device 120 can be a dynamic random-access memory
(DRAM) device, a static random-access memory (SRAM) device, flash
memory device, phase-change memory device, or some other memory
device having suitable performance to serve as process memory. In
one embodiment the memory device 120 can operate as system memory
for the system 100, to store data 122 and instructions 121 for use
when the one or more processors 102 executes an application or
process. Memory controller 116 also couples with an optional
external graphics processor 118, which may communicate with the one
or more graphics processors 108 in processors 102 to perform
graphics and media operations. In some embodiments, graphics,
media, and or compute operations may be assisted by an accelerator
112 which is a coprocessor that can be configured to perform a
specialized set of graphics, media, or compute operations. For
example, in one embodiment the accelerator 112 is a matrix
multiplication accelerator used to optimize machine learning or
compute operations. In one embodiment the accelerator 112 is a
ray-tracing accelerator that can be used to perform ray-tracing
operations in concert with the graphics processor 108. In one
embodiment, an external accelerator 119 may be used in place of or
in concert with the accelerator 112.
[0036] In some embodiments a display device 111 can connect to the
processor(s) 102. The display device 111 can be one or more of an
internal display device, as in a mobile electronic device or a
laptop device or an external display device attached via a display
interface (e.g., DisplayPort, etc.). In one embodiment the display
device 111 can be a head mounted display (HMD) such as a
stereoscopic display device for use in virtual reality (VR)
applications or augmented reality (AR) applications.
[0037] In some embodiments the platform controller hub 130 enables
peripherals to connect to memory device 120 and processor 102 via a
high-speed I/O bus. The I/O peripherals include, but are not
limited to, an audio controller 146, a network controller 134, a
firmware interface 128, a wireless transceiver 126, touch sensors
125, a data storage device 124 (e.g., non-volatile memory, volatile
memory, hard disk drive, flash memory, NAND, 3D NAND, 3D XPoint,
etc.). The data storage device 124 can connect via a storage
interface (e.g., SATA) or via a peripheral bus, such as a
Peripheral Component Interconnect bus (e.g., PCI, PCI express). The
touch sensors 125 can include touch screen sensors, pressure
sensors, or fingerprint sensors. The wireless transceiver 126 can
be a Wi-Fi transceiver, a Bluetooth transceiver, or a mobile
network transceiver such as a 3G, 4G, 5G, or Long-Term Evolution
(LTE) transceiver. The firmware interface 128 enables communication
with system firmware, and can be, for example, a unified extensible
firmware interface (UEFI). The network controller 134 can enable a
network connection to a wired network. In some embodiments, a
high-performance network controller (not shown) couples with the
interface bus 110. The audio controller 146, in one embodiment, is
a multi-channel high definition audio controller. In one embodiment
the system 100 includes an optional legacy I/O controller 140 for
coupling legacy (e.g., Personal System 2 (PS/2)) devices to the
system. The platform controller hub 130 can also connect to one or
more Universal Serial Bus (USB) controllers 142 connect input
devices, such as keyboard and mouse 143 combinations, a camera 144,
or other USB input devices.
[0038] It will be appreciated that the system 100 shown is
exemplary and not limiting, as other types of data processing
systems that are differently configured may also be used. For
example, an instance of the memory controller 116 and platform
controller hub 130 may be integrated into a discreet external
graphics processor, such as the external graphics processor 118. In
one embodiment the platform controller hub 130 and/or memory
controller 116 may be external to the one or more processor(s) 102.
For example, the system 100 can include an external memory
controller 116 and platform controller hub 130, which may be
configured as a memory controller hub and peripheral controller hub
within a system chipset that is in communication with the
processor(s) 102.
[0039] For example, circuit boards ("sleds") can be used on which
components such as CPUs, memory, and other components are placed
are designed for increased thermal performance. In some examples,
processing components such as the processors are located on a top
side of a sled while near memory, such as DIMMs, are located on a
bottom side of the sled. As a result of the enhanced airflow
provided by this design, the components may operate at higher
frequencies and power levels than in typical systems, thereby
increasing performance. Furthermore, the sleds are configured to
blindly mate with power and data communication cables in a rack,
thereby enhancing their ability to be quickly removed, upgraded,
reinstalled, and/or replaced. Similarly, individual components
located on the sleds, such as processors, accelerators, memory, and
data storage drives, are configured to be easily upgraded due to
their increased spacing from each other. In the illustrative
embodiment, the components additionally include hardware
attestation features to prove their authenticity.
[0040] A data center can utilize a single network architecture
("fabric") that supports multiple other network architectures
including Ethernet and Omni-Path. The sleds can be coupled to
switches via optical fibers, which provide higher bandwidth and
lower latency than typical twisted pair cabling (e.g., Category 5,
Category 5e, Category 6, etc.). Due to the high bandwidth, low
latency interconnections and network architecture, the data center
may, in use, pool resources, such as memory, accelerators (e.g.,
GPUs, graphics accelerators, FPGAs, ASICs, neural network and/or
artificial intelligence accelerators, etc.), and data storage
drives that are physically disaggregated, and provide them to
compute resources (e.g., processors) on an as needed basis,
enabling the compute resources to access the pooled resources as if
they were local.
[0041] A power supply or source can provide voltage and/or current
to system 100 or any component or system described herein. In one
example, the power supply includes an AC to DC (alternating current
to direct current) adapter to plug into a wall outlet. Such AC
power can be renewable energy (e.g., solar power) power source. In
one example, power source includes a DC power source, such as an
external AC to DC converter. In one example, power source or power
supply includes wireless charging hardware to charge via proximity
to a charging field. In one example, power source can include an
internal battery, alternating current supply, motion-based power
supply, solar power supply, or fuel cell source.
[0042] FIGS. 2A-2D illustrate computing systems and graphics
processors provided by embodiments described herein. The elements
of FIGS. 2A-2D having the same reference numbers (or names) as the
elements of any other figure herein can operate or function in any
manner similar to that described elsewhere herein, but are not
limited to such.
[0043] FIG. 2A is a block diagram of an embodiment of a processor
200 having one or more processor cores 202A-202N, an integrated
memory controller 214, and an integrated graphics processor 208.
Processor 200 can include additional cores up to and including
additional core 202N represented by the dashed lined boxes. Each of
processor cores 202A-202N includes one or more internal cache units
204A-204N. In some embodiments each processor core also has access
to one or more shared cached units 206. The internal cache units
204A-204N and shared cache units 206 represent a cache memory
hierarchy within the processor 200. The cache memory hierarchy may
include at least one level of instruction and data cache within
each processor core and one or more levels of shared mid-level
cache, such as a Level 2 (L2), Level 3 (L3), Level 4 (L4), or other
levels of cache, where the highest level of cache before external
memory is classified as the LLC. In some embodiments, cache
coherency logic maintains coherency between the various cache units
206 and 204A-204N.
[0044] In some embodiments, processor 200 may also include a set of
one or more bus controller units 216 and a system agent core 210.
The one or more bus controller units 216 manage a set of peripheral
buses, such as one or more PCI or PCI express busses. System agent
core 210 provides management functionality for the various
processor components. In some embodiments, system agent core 210
includes one or more integrated memory controllers 214 to manage
access to various external memory devices (not shown).
[0045] In some embodiments, one or more of the processor cores
202A-202N include support for simultaneous multi-threading. In such
embodiment, the system agent core 210 includes components for
coordinating and operating cores 202A-202N during multi-threaded
processing. System agent core 210 may additionally include a power
control unit (PCU), which includes logic and components to regulate
the power state of processor cores 202A-202N and graphics processor
208.
[0046] In some embodiments, processor 200 additionally includes
graphics processor 208 to execute graphics processing operations.
In some embodiments, the graphics processor 208 couples with the
set of shared cache units 206, and the system agent core 210,
including the one or more integrated memory controllers 214. In
some embodiments, the system agent core 210 also includes a display
controller 211 to drive graphics processor output to one or more
coupled displays. In some embodiments, display controller 211 may
also be a separate module coupled with the graphics processor via
at least one interconnect, or may be integrated within the graphics
processor 208.
[0047] In some embodiments, a ring-based interconnect unit 212 is
used to couple the internal components of the processor 200.
However, an alternative interconnect unit may be used, such as a
point-to-point interconnect, a switched interconnect, or other
techniques, including techniques well known in the art. In some
embodiments, graphics processor 208 couples with the ring
interconnect 212 via an I/O link 213.
[0048] The exemplary I/O link 213 represents at least one of
multiple varieties of I/O interconnects, including an on package
I/O interconnect which facilitates communication between various
processor components and a high-performance embedded memory module
218, such as an eDRAM module. In some embodiments, each of the
processor cores 202A-202N and graphics processor 208 can use
embedded memory modules 218 as a shared Last Level Cache.
[0049] In some embodiments, processor cores 202A-202N are
homogenous cores executing the same instruction set architecture.
In another embodiment, processor cores 202A-202N are heterogeneous
in terms of instruction set architecture (ISA), where one or more
of processor cores 202A-202N execute a first instruction set, while
at least one of the other cores executes a subset of the first
instruction set or a different instruction set. In one embodiment,
processor cores 202A-202N are heterogeneous in terms of
microarchitecture, where one or more cores having a relatively
higher power consumption couple with one or more power cores having
a lower power consumption. In one embodiment, processor cores
202A-202N are heterogeneous in terms of computational capability.
Additionally, processor 200 can be implemented on one or more chips
or as an SoC integrated circuit having the illustrated components,
in addition to other components.
[0050] FIG. 2B is a block diagram of hardware logic of a graphics
processor core 219, according to some embodiments described herein.
Elements of FIG. 2B having the same reference numbers (or names) as
the elements of any other figure herein can operate or function in
any manner similar to that described elsewhere herein, but are not
limited to such. The graphics processor core 219, sometimes
referred to as a core slice, can be one or multiple graphics cores
within a modular graphics processor. The graphics processor core
219 is exemplary of one graphics core slice, and a graphics
processor as described herein may include multiple graphics core
slices based on target power and performance envelopes. Each
graphics processor core 219 can include a fixed function block 230
coupled with multiple sub-cores 221A-221F, also referred to as
sub-slices, that include modular blocks of general-purpose and
fixed function logic.
[0051] In some embodiments, the fixed function block 230 includes a
geometry/fixed function pipeline 231 that can be shared by all
sub-cores in the graphics processor core 219, for example, in lower
performance and/or lower power graphics processor implementations.
In various embodiments, the geometry/fixed function pipeline 231
includes a 3D fixed function pipeline (e.g., 3D pipeline 312 as in
FIG. 3 and FIG. 4, described below) a video front-end unit, a
thread spawner and thread dispatcher, and a unified return buffer
manager, which manages unified return buffers (e.g., unified return
buffer 418 in FIG. 4, as described below).
[0052] In one embodiment the fixed function block 230 also includes
a graphics SoC interface 232, a graphics microcontroller 233, and a
media pipeline 234. The graphics SoC interface 232 provides an
interface between the graphics processor core 219 and other
processor cores within a system on a chip integrated circuit. The
graphics microcontroller 233 is a programmable sub-processor that
is configurable to manage various functions of the graphics
processor core 219, including thread dispatch, scheduling, and
pre-emption. The media pipeline 234 (e.g., media pipeline 316 of
FIG. 3 and FIG. 4) includes logic to facilitate the decoding,
encoding, pre-processing, and/or post-processing of multimedia
data, including image and video data. The media pipeline 234
implement media operations via requests to compute or sampling
logic within the sub-cores 221-221F.
[0053] In one embodiment the SoC interface 232 enables the graphics
processor core 219 to communicate with general-purpose application
processor cores (e.g., CPUs) and/or other components within an SoC,
including memory hierarchy elements such as a shared last level
cache memory, the system RAM, and/or embedded on-chip or on-package
DRAM. The SoC interface 232 can also enable communication with
fixed function devices within the SoC, such as camera imaging
pipelines, and enables the use of and/or implements global memory
atomics that may be shared between the graphics processor core 219
and CPUs within the SoC. The SoC interface 232 can also implement
power management controls for the graphics processor core 219 and
enable an interface between a clock domain of the graphic core 219
and other clock domains within the SoC. In one embodiment the SoC
interface 232 enables receipt of command buffers from a command
streamer and global thread dispatcher that are configured to
provide commands and instructions to each of one or more graphics
cores within a graphics processor. The commands and instructions
can be dispatched to the media pipeline 234, when media operations
are to be performed, or a geometry and fixed function pipeline
(e.g., geometry and fixed function pipeline 231, geometry and fixed
function pipeline 237) when graphics processing operations are to
be performed.
[0054] The graphics microcontroller 233 can be configured to
perform various scheduling and management tasks for the graphics
processor core 219. In one embodiment the graphics microcontroller
233 can perform graphics and/or compute workload scheduling on the
various graphics parallel engines within processing resource (e.g.,
execution unit (EU)) arrays 222A-222F, 224A-224F within the
sub-cores 221A-221F. In this scheduling model, host software
executing on a CPU core of an SoC including the graphics processor
core 219 can submit workloads one of multiple graphic processor
doorbells, which invokes a scheduling operation on the appropriate
graphics engine. Scheduling operations include determining which
workload to run next, submitting a workload to a command streamer,
pre-empting existing workloads running on an engine, monitoring
progress of a workload, and notifying host software when a workload
is complete. In one embodiment the graphics microcontroller 233 can
also facilitate low-power or idle states for the graphics processor
core 219, providing the graphics processor core 219 with the
ability to save and restore registers within the graphics processor
core 219 across low-power state transitions independently from the
operating system and/or graphics driver software on the system.
[0055] The graphics processor core 219 may have greater than or
fewer than the illustrated sub-cores 221A-221F, up to N modular
sub-cores. For each set of N sub-cores, the graphics processor core
219 can also include shared function logic 235, shared and/or cache
memory 236, a geometry/fixed function pipeline 237, as well as
additional fixed function logic 238 to accelerate various graphics
and compute processing operations. The shared function logic 235
can include logic units associated with the shared function logic
420 of FIG. 4 (e.g., sampler, math, and/or inter-thread
communication logic) that can be shared by each N sub-cores within
the graphics processor core 219. The shared and/or cache memory 236
can be a last-level cache for the set of N sub-cores 221A-221F
within the graphics processor core 219, and can also serve as
shared memory that is accessible by multiple sub-cores. The
geometry/fixed function pipeline 237 can be included instead of the
geometry/fixed function pipeline 231 within the fixed function
block 230 and can include the same or similar logic units.
[0056] In one embodiment the graphics processor core 219 includes
additional fixed function logic 238 that can include various fixed
function acceleration logic for use by the graphics processor core
219. In one embodiment the additional fixed function logic 238
includes an additional geometry pipeline for use in position only
shading. In position-only shading, two geometry pipelines exist,
the full geometry pipeline within the geometry/fixed function
pipeline 238, 231, and a cull pipeline, which is an additional
geometry pipeline which may be included within the additional fixed
function logic 238. In one embodiment the cull pipeline is a
trimmed down version of the full geometry pipeline. The full
pipeline and the cull pipeline can execute different instances of
the same application, each instance having a separate context.
Position only shading can hide long cull runs of discarded
triangles, enabling shading to be completed earlier in some
instances. For example and in one embodiment the cull pipeline
logic within the additional fixed function logic 238 can execute
position shaders in parallel with the main application and
generally generates critical results faster than the full pipeline,
as the cull pipeline fetches and shades only the position attribute
of the vertices, without performing rasterization and rendering of
the pixels to the frame buffer. The cull pipeline can use the
generated critical results to compute visibility information for
all the triangles without regard to whether those triangles are
culled. The full pipeline (which in this instance may be referred
to as a replay pipeline) can consume the visibility information to
skip the culled triangles to shade only the visible triangles that
are finally passed to the rasterization phase.
[0057] In one embodiment the additional fixed function logic 238
can also include machine-learning acceleration logic, such as fixed
function matrix multiplication logic, for implementations including
optimizations for machine learning training or inferencing.
[0058] Within each graphics sub-core 221A-221F includes a set of
execution resources that may be used to perform graphics, media,
and compute operations in response to requests by graphics
pipeline, media pipeline, or shader programs. The graphics
sub-cores 221A-221F include multiple EU arrays 222A-222F,
224A-224F, thread dispatch and inter-thread communication (TD/IC)
logic 223A-223F, a 3D (e.g., texture) sampler 225A-225F, a media
sampler 206A-206F, a shader processor 227A-227F, and shared local
memory (SLM) 228A-228F. The EU arrays 222A-222F, 224A-224F each
include multiple execution units, which are general-purpose
graphics processing units capable of performing floating-point and
integer/fixed-point logic operations in service of a graphics,
media, or compute operation, including graphics, media, or compute
shader programs. The TD/IC logic 223A-223F performs local thread
dispatch and thread control operations for the execution units
within a sub-core and facilitate communication between threads
executing on the execution units of the sub-core. The 3D sampler
225A-225F can read texture or other 3D graphics related data into
memory. The 3D sampler can read texture data differently based on a
configured sample state and the texture format associated with a
given texture. The media sampler 206A-206F can perform similar read
operations based on the type and format associated with media data.
In one embodiment, each graphics sub-core 221A-221F can alternately
include a unified 3D and media sampler. Threads executing on the
execution units within each of the sub-cores 221A-221F can make use
of shared local memory 228A-228F within each sub-core, to enable
threads executing within a thread group to execute using a common
pool of on-chip memory.
[0059] FIG. 2C illustrates a graphics processing unit (GPU) 239
that includes dedicated sets of graphics processing resources
arranged into multi-core groups 240A-240N. While the details of
only a single multi-core group 240A are provided, it will be
appreciated that the other multi-core groups 240B-240N may be
equipped with the same or similar sets of graphics processing
resources.
[0060] As illustrated, a multi-core group 240A may include a set of
graphics cores 243, a set of tensor cores 244, and a set of ray
tracing cores 245. A scheduler/dispatcher 241 schedules and
dispatches the graphics threads for execution on the various cores
243, 244, 245. A set of register files 242 store operand values
used by the cores 243, 244, 245 when executing the graphics
threads. These may include, for example, integer registers for
storing integer values, floating point registers for storing
floating point values, vector registers for storing packed data
elements (integer and/or floating point data elements) and tile
registers for storing tensor/matrix values. In one embodiment, the
tile registers are implemented as combined sets of vector
registers.
[0061] One or more combined level 1 (L1) caches and shared memory
units 247 store graphics data such as texture data, vertex data,
pixel data, ray data, bounding volume data, etc., locally within
each multi-core group 240A. One or more texture units 247 can also
be used to perform texturing operations, such as texture mapping
and sampling. A Level 2 (L2) cache 253 shared by all or a subset of
the multi-core groups 240A-240N stores graphics data and/or
instructions for multiple concurrent graphics threads. As
illustrated, the L2 cache 253 may be shared across a plurality of
multi-core groups 240A-240N. One or more memory controllers 248
couple the GPU 239 to a memory 249 which may be a system memory
(e.g., DRAM) and/or a dedicated graphics memory (e.g., GDDR6
memory).
[0062] Input/output (I/O) circuitry 250 couples the GPU 239 to one
or more I/O devices 252 such as digital signal processors (DSPs),
network controllers, or user input devices. An on-chip interconnect
may be used to couple the I/O devices 252 to the GPU 239 and memory
249. One or more I/O memory management units (IOMMUs) 251 of the
I/O circuitry 250 couple the I/O devices 252 directly to the system
memory 249. In one embodiment, the IOMMU 251 manages multiple sets
of page tables to map virtual addresses to physical addresses in
system memory 249. In this embodiment, the I/O devices 252, CPU(s)
246, and GPU(s) 239 may share the same virtual address space.
[0063] In one implementation, the IOMMU 251 supports
virtualization. In this case, it may manage a first set of page
tables to map guest/graphics virtual addresses to guest/graphics
physical addresses and a second set of page tables to map the
guest/graphics physical addresses to system/host physical addresses
(e.g., within system memory 249). The base addresses of each of the
first and second sets of page tables may be stored in control
registers and swapped out on a context switch (e.g., so that the
new context is provided with access to the relevant set of page
tables). While not illustrated in FIG. 2C, each of the cores 243,
244, 245 and/or multi-core groups 240A-240N may include translation
lookaside buffers (TLBs) to cache guest virtual to guest physical
translations, guest physical to host physical translations, and
guest virtual to host physical translations.
[0064] In one embodiment, the CPUs 246, GPUs 239, and I/O devices
252 are integrated on a single semiconductor chip and/or chip
package. The illustrated memory 249 may be integrated on the same
chip or may be coupled to the memory controllers 248 via an
off-chip interface. In one implementation, the memory 249 comprises
GDDR6 memory which shares the same virtual address space as other
physical system-level memories, although the underlying principles
of the invention are not limited to this specific
implementation.
[0065] In one embodiment, the tensor cores 244 include a plurality
of execution units specifically designed to perform matrix
operations, which are the fundamental compute operation used to
perform deep learning operations. For example, simultaneous matrix
multiplication operations may be used for neural network training
and inferencing. The tensor cores 244 may perform matrix processing
using a variety of operand precisions including single precision
floating-point (e.g., 32 bits), half-precision floating point
(e.g., 16 bits), integer words (16 bits), bytes (8 bits), and
half-bytes (4 bits). In one embodiment, a neural network
implementation extracts features of each rendered scene,
potentially combining details from multiple frames, to construct a
high-quality final image.
[0066] In deep learning implementations, parallel matrix
multiplication work may be scheduled for execution on the tensor
cores 244. The training of neural networks, in particular, requires
a significant number matrix dot product operations. In order to
process an inner-product formulation of an N.times.N.times.N matrix
multiply, the tensor cores 244 may include at least N dot-product
processing elements. Before the matrix multiply begins, one entire
matrix is loaded into tile registers and at least one column of a
second matrix is loaded each cycle for N cycles. Each cycle, there
are N dot products that are processed.
[0067] Matrix elements may be stored at different precisions
depending on the particular implementation, including 16-bit words,
8-bit bytes (e.g., INT8) and 4-bit half-bytes (e.g., INT4).
Different precision modes may be specified for the tensor cores 244
to ensure that the most efficient precision is used for different
workloads (e.g., such as inferencing workloads which can tolerate
quantization to bytes and half-bytes).
[0068] In one embodiment, the ray tracing cores 245 accelerate ray
tracing operations for both real-time ray tracing and non-real-time
ray tracing implementations. In particular, the ray tracing cores
245 include ray traversal/intersection circuitry for performing ray
traversal using bounding volume hierarchies (BVHs) and identifying
intersections between rays and primitives enclosed within the BVH
volumes. The ray tracing cores 245 may also include circuitry for
performing depth testing and culling (e.g., using a Z buffer or
similar arrangement). In one implementation, the ray tracing cores
245 perform traversal and intersection operations in concert with
the image denoising techniques described herein, at least a portion
of which may be executed on the tensor cores 244. For example, in
one embodiment, the tensor cores 244 implement a deep learning
neural network to perform denoising of frames generated by the ray
tracing cores 245. However, the CPU(s) 246, graphics cores 243,
and/or ray tracing cores 245 may also implement all or a portion of
the denoising and/or deep learning algorithms.
[0069] In addition, as described above, a distributed approach to
denoising may be employed in which the GPU 239 is in a computing
device coupled to other computing devices over a network or high
speed interconnect. In this embodiment, the interconnected
computing devices share neural network learning/training data to
improve the speed with which the overall system learns to perform
denoising for different types of image frames and/or different
graphics applications.
[0070] In one embodiment, the ray tracing cores 245 process all BVH
traversal and ray-primitive intersections, saving the graphics
cores 243 from being overloaded with thousands of instructions per
ray. In one embodiment, each ray tracing core 245 includes a first
set of specialized circuitry for performing bounding box tests
(e.g., for traversal operations) and a second set of specialized
circuitry for performing the ray-triangle intersection tests (e.g.,
intersecting rays which have been traversed). Thus, in one
embodiment, the multi-core group 240A can simply launch a ray
probe, and the ray tracing cores 245 independently perform ray
traversal and intersection and return hit data (e.g., a hit, no
hit, multiple hits, etc.) to the thread context. The other cores
243, 244 are freed to perform other graphics or compute work while
the ray tracing cores 245 perform the traversal and intersection
operations.
[0071] In one embodiment, each ray tracing core 245 includes a
traversal unit to perform BVH testing operations and an
intersection unit which performs ray-primitive intersection tests.
The intersection unit generates a "hit", "no hit", or "multiple
hit" response, which it provides to the appropriate thread. During
the traversal and intersection operations, the execution resources
of the other cores (e.g., graphics cores 243 and tensor cores 244)
are freed to perform other forms of graphics work.
[0072] In one particular embodiment described below, a hybrid
rasterization/ray tracing approach is used in which work is
distributed between the graphics cores 243 and ray tracing cores
245.
[0073] In one embodiment, the ray tracing cores 245 (and/or other
cores 243, 244) include hardware support for a ray tracing
instruction set such as Microsoft's DirectX Ray Tracing (DXR) which
includes a DispatchRays command, as well as ray-generation,
closest-hit, any-hit, and miss shaders, which enable the assignment
of unique sets of shaders and textures for each object. Another ray
tracing platform which may be supported by the ray tracing cores
245, graphics cores 243 and tensor cores 244 is Vulkan 1.1.85.
Note, however, that the underlying principles of the invention are
not limited to any particular ray tracing ISA.
[0074] In general, the various cores 245, 244, 243 may support a
ray tracing instruction set that includes instructions/functions
for ray generation, closest hit, any hit, ray-primitive
intersection, per-primitive and hierarchical bounding box
construction, miss, visit, and exceptions. More specifically, one
embodiment includes ray tracing instructions to perform the
following functions:
[0075] Ray Generation--Ray generation instructions may be executed
for each pixel, sample, or other user-defined work assignment.
[0076] Closest Hit--A closest hit instruction may be executed to
locate the closest intersection point of a ray with primitives
within a scene.
[0077] Any Hit--An any hit instruction identifies multiple
intersections between a ray and primitives within a scene,
potentially to identify a new closest intersection point.
[0078] Intersection--An intersection instruction performs a
ray-primitive intersection test and outputs a result.
[0079] Per-primitive Bounding box Construction--This instruction
builds a bounding box around a given primitive or group of
primitives (e.g., when building a new BVH or other acceleration
data structure).
[0080] Miss--Indicates that a ray misses all geometry within a
scene, or specified region of a scene.
[0081] Visit--Indicates the children volumes a ray will
traverse.
[0082] Exceptions--Includes various types of exception handlers
(e.g., invoked for various error conditions).
[0083] FIG. 2D is a block diagram of general purpose graphics
processing unit (GPGPU) 270 that can be configured as a graphics
processor and/or compute accelerator, according to embodiments
described herein. The GPGPU 270 can interconnect with host
processors (e.g., one or more CPU(s) 246) and memory 271, 272 via
one or more system and/or memory busses. In one embodiment the
memory 271 is system memory that may be shared with the one or more
CPU(s) 246, while memory 272 is device memory that is dedicated to
the GPGPU 270. In one embodiment, components within the GPGPU 270
and device memory 272 may be mapped into memory addresses that are
accessible to the one or more CPU(s) 246. Access to memory 271 and
272 may be facilitated via a memory controller 268. In one
embodiment the memory controller 268 includes an internal direct
memory access (DMA) controller 269 or can include logic to perform
operations that would otherwise be performed by a DMA
controller.
[0084] The GPGPU 270 includes multiple cache memories, including an
L2 cache 253, L1 cache 254, an instruction cache 255, and shared
memory 256, at least a portion of which may also be partitioned as
a cache memory. The GPGPU 270 also includes multiple compute units
260A-260N. Each compute unit 260A-260N includes a set of vector
registers 261, scalar registers 262, vector logic units 263, and
scalar logic units 264. The compute units 260A-260N can also
include local shared memory 265 and a program counter 266. The
compute units 260A-260N can couple with a constant cache 267, which
can be used to store constant data, which is data that will not
change during the run of kernel or shader program that executes on
the GPGPU 270. In one embodiment the constant cache 267 is a scalar
data cache and cached data can be fetched directly into the scalar
registers 262.
[0085] During operation, the one or more CPU(s) 246 can write
commands into registers or memory in the GPGPU 270 that has been
mapped into an accessible address space. The command processors 257
can read the commands from registers or memory and determine how
those commands will be processed within the GPGPU 270. A thread
dispatcher 258 can then be used to dispatch threads to the compute
units 260A-260N to perform those commands. Each compute unit
260A-260N can execute threads independently of the other compute
units. Additionally each compute unit 260A-260N can be
independently configured for conditional computation and can
conditionally output the results of computation to memory. The
command processors 257 can interrupt the one or more CPU(s) 246
when the submitted commands are complete.
[0086] FIGS. 3A-3C illustrate block diagrams of additional graphics
processor and compute accelerator architectures provided by
embodiments described herein. The elements of FIGS. 3A-3C having
the same reference numbers (or names) as the elements of any other
figure herein can operate or function in any manner similar to that
described elsewhere herein, but are not limited to such.
[0087] FIG. 3A is a block diagram of a graphics processor 300,
which may be a discrete graphics processing unit, or may be a
graphics processor integrated with a plurality of processing cores,
or other semiconductor devices such as, but not limited to, memory
devices or network interfaces. In some embodiments, the graphics
processor communicates via a memory mapped I/O interface to
registers on the graphics processor and with commands placed into
the processor memory. In some embodiments, graphics processor 300
includes a memory interface 314 to access memory. Memory interface
314 can be an interface to local memory, one or more internal
caches, one or more shared external caches, and/or to system
memory.
[0088] In some embodiments, graphics processor 300 also includes a
display controller 302 to drive display output data to a display
device 318. Display controller 302 includes hardware for one or
more overlay planes for the display and composition of multiple
layers of video or user interface elements. The display device 318
can be an internal or external display device. In one embodiment
the display device 318 is a head mounted display device, such as a
virtual reality (VR) display device or an augmented reality (AR)
display device. In some embodiments, graphics processor 300
includes a video codec engine 306 to encode, decode, or transcode
media to, from, or between one or more media encoding formats,
including, but not limited to Moving Picture Experts Group (MPEG)
formats such as MPEG-2, Advanced Video Coding (AVC) formats such as
H.264/MPEG-4 AVC, H.265/HEVC, Alliance for Open Media (AOMedia)
VP8, VP9, as well as the Society of Motion Picture & Television
Engineers (SMPTE) 421M/VC-1, and Joint Photographic Experts Group
(JPEG) formats such as JPEG, and Motion JPEG (MJPEG) formats.
[0089] In some embodiments, graphics processor 300 includes a block
image transfer (BLIT) engine 304 to perform two-dimensional (2D)
rasterizer operations including, for example, bit-boundary block
transfers. However, in one embodiment, 2D graphics operations are
performed using one or more components of graphics processing
engine (GPE) 310. In some embodiments, GPE 310 is a compute engine
for performing graphics operations, including three-dimensional
(3D) graphics operations and media operations.
[0090] In some embodiments, GPE 310 includes a 3D pipeline 312 for
performing 3D operations, such as rendering three-dimensional
images and scenes using processing functions that act upon 3D
primitive shapes (e.g., rectangle, triangle, etc.). The 3D pipeline
312 includes programmable and fixed function elements that perform
various tasks within the element and/or spawn execution threads to
a 3D/Media sub-system 315. While 3D pipeline 312 can be used to
perform media operations, an embodiment of GPE 310 also includes a
media pipeline 316 that is specifically used to perform media
operations, such as video post-processing and image
enhancement.
[0091] In some embodiments, media pipeline 316 includes fixed
function or programmable logic units to perform one or more
specialized media operations, such as video decode acceleration,
video de-interlacing, and video encode acceleration in place of, or
on behalf of video codec engine 306. In some embodiments, media
pipeline 316 additionally includes a thread spawning unit to spawn
threads for execution on 3D/Media sub-system 315. The spawned
threads perform computations for the media operations on one or
more graphics execution units included in 3D/Media sub-system
315.
[0092] In some embodiments, 3D/Media subsystem 315 includes logic
for executing threads spawned by 3D pipeline 312 and media pipeline
316. In one embodiment, the pipelines send thread execution
requests to 3D/Media subsystem 315, which includes thread dispatch
logic for arbitrating and dispatching the various requests to
available thread execution resources. The execution resources
include an array of graphics execution units to process the 3D and
media threads. In some embodiments, 3D/Media subsystem 315 includes
one or more internal caches for thread instructions and data. In
some embodiments, the subsystem also includes shared memory,
including registers and addressable memory, to share data between
threads and to store output data.
[0093] FIG. 3B illustrates a graphics processor 320 having a tiled
architecture, according to embodiments described herein. In one
embodiment the graphics processor 320 includes a graphics
processing engine cluster 322 having multiple instances of the
graphics processing engine 310 of FIG. 3A within a graphics engine
tile 310A-310D. Each graphics engine tile 310A-310D can be
interconnected via a set of tile interconnects 323A-323F. Each
graphics engine tile 310A-310D can also be connected to a memory
module or memory device 326A-326D via memory interconnects
325A-325D. The memory devices 326A-326D can use any graphics memory
technology. For example, the memory devices 326A-326D may be
graphics double data rate (GDDR) memory. The memory devices
326A-326D, in one embodiment, are high-bandwidth memory (HBM)
modules that can be on-die with their respective graphics engine
tile 310A-310D. In one embodiment the memory devices 326A-326D are
stacked memory devices that can be stacked on top of their
respective graphics engine tile 310A-310D. In one embodiment, each
graphics engine tile 310A-310D and associated memory 326A-326D
reside on separate chiplets, which are bonded to a base die or base
substrate, as described on further detail in FIGS. 11B-11D.
[0094] The graphics processing engine cluster 322 can connect with
an on-chip or on-package fabric interconnect 324. The fabric
interconnect 324 can enable communication between graphics engine
tiles 310A-310D and components such as the video codec 306 and one
or more copy engines 304. The copy engines 304 can be used to move
data out of, into, and between the memory devices 326A-326D and
memory that is external to the graphics processor 320 (e.g., system
memory). The fabric interconnect 324 can also be used to
interconnect the graphics engine tiles 310A-310D. The graphics
processor 320 may optionally include a display controller 302 to
enable a connection with an external display device 318. The
graphics processor may also be configured as a graphics or compute
accelerator. In the accelerator configuration, the display
controller 302 and display device 318 may be omitted.
[0095] The graphics processor 320 can connect to a host system via
a host interface 328. The host interface 328 can enable
communication between the graphics processor 320, system memory,
and/or other system components. The host interface 328 can be, for
example a PCI express bus or another type of host system
interface.
[0096] FIG. 3C illustrates a compute accelerator 330, according to
embodiments described herein. The compute accelerator 330 can
include architectural similarities with the graphics processor 320
of FIG. 3B and is optimized for compute acceleration. A compute
engine cluster 332 can include a set of compute engine tiles
340A-340D that include execution logic that is optimized for
parallel or vector-based general-purpose compute operations. In
some embodiments, the compute engine tiles 340A-340D do not include
fixed function graphics processing logic, although in one
embodiment one or more of the compute engine tiles 340A-340D can
include logic to perform media acceleration. The compute engine
tiles 340A-340D can connect to memory 326A-326D via memory
interconnects 325A-325D. The memory 326A-326D and memory
interconnects 325A-325D may be similar technology as in graphics
processor 320, or can be different. The graphics compute engine
tiles 340A-340D can also be interconnected via a set of tile
interconnects 323A-323F and may be connected with and/or
interconnected by a fabric interconnect 324. In one embodiment the
compute accelerator 330 includes a large L3 cache 336 that can be
configured as a device-wide cache. The compute accelerator 330 can
also connect to a host processor and memory via a host interface
328 in a similar manner as the graphics processor 320 of FIG.
3B.
[0097] Graphics Processing Engine
[0098] FIG. 4 is a block diagram of a graphics processing engine
410 of a graphics processor in accordance with some embodiments. In
one embodiment, the graphics processing engine (GPE) 410 is a
version of the GPE 310 shown in FIG. 3A, and may also represent a
graphics engine tile 310A-310D of FIG. 3B. Elements of FIG. 4
having the same reference numbers (or names) as the elements of any
other figure herein can operate or function in any manner similar
to that described elsewhere herein, but are not limited to such.
For example, the 3D pipeline 312 and media pipeline 316 of FIG. 3A
are illustrated. The media pipeline 316 is optional in some
embodiments of the GPE 410 and may not be explicitly included
within the GPE 410. For example and in at least one embodiment, a
separate media and/or image processor is coupled to the GPE
410.
[0099] In some embodiments, GPE 410 couples with or includes a
command streamer 403, which provides a command stream to the 3D
pipeline 312 and/or media pipelines 316. In some embodiments,
command streamer 403 is coupled with memory, which can be system
memory, or one or more of internal cache memory and shared cache
memory. In some embodiments, command streamer 403 receives commands
from the memory and sends the commands to 3D pipeline 312 and/or
media pipeline 316. The commands are directives fetched from a ring
buffer, which stores commands for the 3D pipeline 312 and media
pipeline 316. In one embodiment, the ring buffer can additionally
include batch command buffers storing batches of multiple commands.
The commands for the 3D pipeline 312 can also include references to
data stored in memory, such as but not limited to vertex and
geometry data for the 3D pipeline 312 and/or image data and memory
objects for the media pipeline 316. The 3D pipeline 312 and media
pipeline 316 process the commands and data by performing operations
via logic within the respective pipelines or by dispatching one or
more execution threads to a graphics core array 414. In one
embodiment the graphics core array 414 include one or more blocks
of graphics cores (e.g., graphics core(s) 415A, graphics core(s)
415B), each block including one or more graphics cores. Each
graphics core includes a set of graphics execution resources that
includes general-purpose and graphics specific execution logic to
perform graphics and compute operations, as well as fixed function
texture processing and/or machine learning and artificial
intelligence acceleration logic.
[0100] In various embodiments the 3D pipeline 312 can include fixed
function and programmable logic to process one or more shader
programs, such as vertex shaders, geometry shaders, pixel shaders,
fragment shaders, compute shaders, or other shader programs, by
processing the instructions and dispatching execution threads to
the graphics core array 414. The graphics core array 414 provides a
unified block of execution resources for use in processing these
shader programs. Multi-purpose execution logic (e.g., execution
units) within the graphics core(s) 415A-414B of the graphic core
array 414 includes support for various 3D API shader languages and
can execute multiple simultaneous execution threads associated with
multiple shaders.
[0101] In some embodiments, the graphics core array 414 includes
execution logic to perform media functions, such as video and/or
image processing. In one embodiment, the execution units include
general-purpose logic that is programmable to perform parallel
general-purpose computational operations, in addition to graphics
processing operations. The general-purpose logic can perform
processing operations in parallel or in conjunction with
general-purpose logic within the processor core(s) 107 of FIG. 1 or
core 202A-202N as in FIG. 2A.
[0102] Output data generated by threads executing on the graphics
core array 414 can output data to memory in a unified return buffer
(URB) 418. The URB 418 can store data for multiple threads. In some
embodiments the URB 418 may be used to send data between different
threads executing on the graphics core array 414. In some
embodiments the URB 418 may additionally be used for
synchronization between threads on the graphics core array and
fixed function logic within the shared function logic 420.
[0103] In some embodiments, graphics core array 414 is scalable,
such that the array includes a variable number of graphics cores,
each having a variable number of execution units based on the
target power and performance level of GPE 410. In one embodiment
the execution resources are dynamically scalable, such that
execution resources may be enabled or disabled as needed.
[0104] The graphics core array 414 couples with shared function
logic 420 that includes multiple resources that are shared between
the graphics cores in the graphics core array. The shared functions
within the shared function logic 420 are hardware logic units that
provide specialized supplemental functionality to the graphics core
array 414. In various embodiments, shared function logic 420
includes but is not limited to sampler 421, math 422, and
inter-thread communication (ITC) 423 logic. Additionally, some
embodiments implement one or more cache(s) 425 within the shared
function logic 420.
[0105] A shared function is implemented at least in a case where
the demand for a given specialized function is insufficient for
inclusion within the graphics core array 414. Instead a single
instantiation of that specialized function is implemented as a
stand-alone entity in the shared function logic 420 and shared
among the execution resources within the graphics core array 414.
The precise set of functions that are shared between the graphics
core array 414 and included within the graphics core array 414
varies across embodiments. In some embodiments, specific shared
functions within the shared function logic 420 that are used
extensively by the graphics core array 414 may be included within
shared function logic 416 within the graphics core array 414. In
various embodiments, the shared function logic 416 within the
graphics core array 414 can include some or all logic within the
shared function logic 420. In one embodiment, all logic elements
within the shared function logic 420 may be duplicated within the
shared function logic 416 of the graphics core array 414. In one
embodiment the shared function logic 420 is excluded in favor of
the shared function logic 416 within the graphics core array
414.
[0106] Execution Units
[0107] FIGS. 5A-5B illustrate thread execution logic 500 including
an array of processing elements employed in a graphics processor
core according to embodiments described herein. Elements of FIGS.
5A-5B having the same reference numbers (or names) as the elements
of any other figure herein can operate or function in any manner
similar to that described elsewhere herein, but are not limited to
such. FIG. 5A-5B illustrates an overview of thread execution logic
500, which may be representative of hardware logic illustrated with
each sub-core 221A-221F of FIG. 2B. FIG. 5A is representative of an
execution unit within a general-purpose graphics processor, while
FIG. 5B is representative of an execution unit that may be used
within a compute accelerator.
[0108] As illustrated in FIG. 5A, in some embodiments thread
execution logic 500 includes a shader processor 502, a thread
dispatcher 504, instruction cache 506, a scalable execution unit
array including a plurality of execution units 508A-508N, a sampler
510, shared local memory 511, a data cache 512, and a data port
514. In one embodiment the scalable execution unit array can
dynamically scale by enabling or disabling one or more execution
units (e.g., any of execution unit 508A, 508B, 508C, 508D, through
508N-1 and 508N) based on the computational requirements of a
workload. In one embodiment the included components are
interconnected via an interconnect fabric that links to each of the
components. In some embodiments, thread execution logic 500
includes one or more connections to memory, such as system memory
or cache memory, through one or more of instruction cache 506, data
port 514, sampler 510, and execution units 508A-508N. In some
embodiments, each execution unit (e.g. 508A) is a stand-alone
programmable general-purpose computational unit that is capable of
executing multiple simultaneous hardware threads while processing
multiple data elements in parallel for each thread. In various
embodiments, the array of execution units 508A-508N is scalable to
include any number individual execution units.
[0109] In some embodiments, the execution units 508A-508N are
primarily used to execute shader programs. A shader processor 502
can process the various shader programs and dispatch execution
threads associated with the shader programs via a thread dispatcher
504. In one embodiment the thread dispatcher includes logic to
arbitrate thread initiation requests from the graphics and media
pipelines and instantiate the requested threads on one or more
execution unit in the execution units 508A-508N. For example, a
geometry pipeline can dispatch vertex, tessellation, or geometry
shaders to the thread execution logic for processing. In some
embodiments, thread dispatcher 504 can also process runtime thread
spawning requests from the executing shader programs.
[0110] In some embodiments, the execution units 508A-508N support
an instruction set that includes native support for many standard
3D graphics shader instructions, such that shader programs from
graphics libraries (e.g., Direct 3D and OpenGL) are executed with a
minimal translation. The execution units support vertex and
geometry processing (e.g., vertex programs, geometry programs,
vertex shaders), pixel processing (e.g., pixel shaders, fragment
shaders) and general-purpose processing (e.g., compute and media
shaders). Each of the execution units 508A-508N is capable of
multi-issue single instruction multiple data (SIMD) execution and
multi-threaded operation enables an efficient execution environment
in the face of higher latency memory accesses. Each hardware thread
within each execution unit has a dedicated high-bandwidth register
file and associated independent thread-state. Execution is
multi-issue per clock to pipelines capable of integer, single and
double precision floating point operations, SIMD branch capability,
logical operations, transcendental operations, and other
miscellaneous operations. While waiting for data from memory or one
of the shared functions, dependency logic within the execution
units 508A-508N causes a waiting thread to sleep until the
requested data has been returned. While the waiting thread is
sleeping, hardware resources may be devoted to processing other
threads. For example, during a delay associated with a vertex
shader operation, an execution unit can perform operations for a
pixel shader, fragment shader, or another type of shader program,
including a different vertex shader. Various embodiments can apply
to use execution by use of Single Instruction Multiple Thread
(SIMT) as an alternate to use of SIMD or in addition to use of
SIMD. Reference to a SIMD core or operation can apply also to SIMT
or apply to SIMD in combination with SIMT.
[0111] Each execution unit in execution units 508A-508N operates on
arrays of data elements. The number of data elements is the
"execution size," or the number of channels for the instruction. An
execution channel is a logical unit of execution for data element
access, masking, and flow control within instructions. The number
of channels may be independent of the number of physical Arithmetic
Logic Units (ALUs) or Floating Point Units (FPUs) for a particular
graphics processor. In some embodiments, execution units 508A-508N
support integer and floating-point data types.
[0112] The execution unit instruction set includes SIMD
instructions. The various data elements can be stored as a packed
data type in a register and the execution unit will process the
various elements based on the data size of the elements. For
example, when operating on a 256-bit wide vector, the 256 bits of
the vector are stored in a register and the execution unit operates
on the vector as four separate 54-bit packed data elements
(Quad-Word (QW) size data elements), eight separate 32-bit packed
data elements (Double Word (DW) size data elements), sixteen
separate 16-bit packed data elements (Word (W) size data elements),
or thirty-two separate 8-bit data elements (byte (B) size data
elements). However, different vector widths and register sizes are
possible.
[0113] In one embodiment one or more execution units can be
combined into a fused execution unit 509A-509N having control logic
(e.g., 507A-507N, 1412) that is common to the fused EUs. The
control logic may include thread functionality, sampling
functionality for sampling a state for each EU that is associated
with the control logic, and a cache aggregate structure (e.g.,
1450A-1450H of FIG. 14). The control logic determines activity data
from the state with the activity data including at least one of
stalls and reason counts for stalling activity, instruction types,
pipeline utilization, thread utilization, or shader activity. The
control logic may include or be associated with a cache aggregate
structure for storing information (e.g., instruction pointer
address, activity data, stall reason data).
[0114] Multiple EUs can be fused into an EU group. Each EU in the
fused EU group can be configured to execute a separate SIMD
hardware thread. The number of EUs in a fused EU group can vary
according to embodiments. Additionally, various SIMD widths can be
performed per-EU, including but not limited to SIMD8, SIMD16, and
SIMD32. Each fused graphics execution unit 509A-509N includes at
least two execution units. For example, fused execution unit 509A
includes a first EU 508A, second EU 508B, and control logic 507A
that is common to the first EU 508A and the second EU 508B. The
control logic 507A controls threads executed on the fused graphics
execution unit 509A, allowing each EU within the fused execution
units 509A-509N to execute using a common instruction pointer
register.
[0115] One or more internal instruction caches (e.g., 506) are
included in the thread execution logic 500 to cache thread
instructions for the execution units. In some embodiments, one or
more data caches (e.g., 512) are included to cache thread data
during thread execution. Threads executing on the execution logic
500 can also store explicitly managed data in the shared local
memory 511. In some embodiments, a sampler 510 is included to
provide texture sampling for 3D operations and media sampling for
media operations. In some embodiments, sampler 510 includes
specialized texture or media sampling functionality to process
texture or media data during the sampling process before providing
the sampled data to an execution unit.
[0116] During execution, the graphics and media pipelines send
thread initiation requests to thread execution logic 500 via thread
spawning and dispatch logic. Once a group of geometric objects has
been processed and rasterized into pixel data, pixel processor
logic (e.g., pixel shader logic, fragment shader logic, etc.)
within the shader processor 502 is invoked to further compute
output information and cause results to be written to output
surfaces (e.g., color buffers, depth buffers, stencil buffers,
etc.). In some embodiments, a pixel shader or fragment shader
calculates the values of the various vertex attributes that are to
be interpolated across the rasterized object. In some embodiments,
pixel processor logic within the shader processor 502 then executes
an application programming interface (API)-supplied pixel or
fragment shader program. To execute the shader program, the shader
processor 502 dispatches threads to an execution unit (e.g., 508A)
via thread dispatcher 504. In some embodiments, shader processor
502 uses texture sampling logic in the sampler 510 to access
texture data in texture maps stored in memory. Arithmetic
operations on the texture data and the input geometry data compute
pixel color data for each geometric fragment, or discards one or
more pixels from further processing.
[0117] In some embodiments, the data port 514 provides a memory
access mechanism for the thread execution logic 500 to output
processed data to memory for further processing on a graphics
processor output pipeline. In some embodiments, the data port 514
includes or couples to one or more cache memories (e.g., data cache
512) to cache data for memory access via the data port.
[0118] In one embodiment, the execution logic 500 can also include
a ray tracer 505 that can provide ray tracing acceleration
functionality. The ray tracer 505 can support a ray tracing
instruction set that includes instructions/functions for ray
generation. The ray tracing instruction set can be similar to or
different from the ray-tracing instruction set supported by the ray
tracing cores 245 in FIG. 2C.
[0119] FIG. 5B illustrates exemplary internal details of an
execution unit 508, according to embodiments. A graphics execution
unit 508 can include an instruction fetch unit 537, a general
register file array (GRF) 524, an architectural register file array
(ARF) 526, a thread arbiter 522, a send unit 530, a branch unit
532, a set of SIMD floating point units (FPUs) 534, and in one
embodiment a set of dedicated integer SIMD ALUs 535. The GRF 524
and ARF 526 includes the set of general register files and
architecture register files associated with each simultaneous
hardware thread that may be active in the graphics execution unit
508. In one embodiment, per thread architectural state is
maintained in the ARF 526, while data used during thread execution
is stored in the GRF 524. The execution state of each thread,
including the instruction pointers for each thread, can be held in
thread-specific registers in the ARF 526.
[0120] In one embodiment the graphics execution unit 508 has an
architecture that is a combination of Simultaneous Multi-Threading
(SMT) and fine-grained Interleaved Multi-Threading (IMT). The
architecture has a modular configuration that can be fine-tuned at
design time based on a target number of simultaneous threads and
number of registers per execution unit, where execution unit
resources are divided across logic used to execute multiple
simultaneous threads. The number of logical threads that may be
executed by the graphics execution unit 508 is not limited to the
number of hardware threads, and multiple logical threads can be
assigned to each hardware thread.
[0121] In one embodiment, the graphics execution unit 508 can
co-issue multiple instructions, which may each be different
instructions. The thread arbiter 522 of the graphics execution unit
thread 508 can dispatch the instructions to one of the send unit
530, branch unit 532, or SIMD FPU(s) 534 for execution. Each
execution thread can access 128 general-purpose registers within
the GRF 524, where each register can store 32 bytes, accessible as
a SIMD 8-element vector of 32-bit data elements. In one embodiment,
each execution unit thread has access to 4 Kbytes within the GRF
524, although embodiments are not so limited, and greater or fewer
register resources may be provided in other embodiments. In one
embodiment the graphics execution unit 508 is partitioned into
seven hardware threads that can independently perform computational
operations, although the number of threads per execution unit can
also vary according to embodiments. For example, in one embodiment
up to 16 hardware threads are supported. In an embodiment in which
seven threads may access 4 Kbytes, the GRF 524 can store a total of
28 Kbytes. Where 16 threads may access 4 Kbytes, the GRF 524 can
store a total of 64 Kbytes. Flexible addressing modes can permit
registers to be addressed together to build effectively wider
registers or to represent strided rectangular block data
structures.
[0122] In one embodiment, memory operations, sampler operations,
and other longer-latency system communications are dispatched via
"send" instructions that are executed by the message passing send
unit 530. In one embodiment, branch instructions are dispatched to
a dedicated branch unit 532 to facilitate SIMD divergence and
eventual convergence.
[0123] In one embodiment the graphics execution unit 508 includes
one or more SIMD floating point units (FPU(s)) 534 to perform
floating-point operations. In one embodiment, the FPU(s) 534 also
support integer computation. In one embodiment the FPU(s) 534 can
SIMD execute up to M number of 32-bit floating-point (or integer)
operations, or SIMD execute up to 2M 16-bit integer or 16-bit
floating-point operations. In one embodiment, at least one of the
FPU(s) provides extended math capability to support high-throughput
transcendental math functions and double precision 54-bit
floating-point. In some embodiments, a set of 8-bit integer SIMD
ALUs 535 are also present, and may be specifically optimized to
perform operations associated with machine learning
computations.
[0124] In one embodiment, arrays of multiple instances of the
graphics execution unit 508 can be instantiated in a graphics
sub-core grouping (e.g., a sub-slice). For scalability, product
architects can choose the exact number of execution units per
sub-core grouping. In one embodiment the execution unit 508 can
execute instructions across a plurality of execution channels. In a
further embodiment, each thread executed on the graphics execution
unit 508 is executed on a different channel.
[0125] FIG. 6 illustrates an additional execution unit 600,
according to an embodiment. The execution unit 600 may be a
compute-optimized execution unit for use in, for example, a compute
engine tile 340A-340D as in FIG. 3C, but is not limited as such.
Variants of the execution unit 600 may also be used in a graphics
engine tile 310A-310D as in FIG. 3B. In one embodiment, the
execution unit 600 includes a thread control unit 601, a thread
state unit 602, an instruction fetch/prefetch unit 603, and an
instruction decode unit 604. The execution unit 600 additionally
includes a register file 606 that stores registers that can be
assigned to hardware threads within the execution unit. The
execution unit 600 additionally includes a send unit 607 and a
branch unit 608. In one embodiment, the send unit 607 and branch
unit 608 can operate similarly as the send unit 530 and a branch
unit 532 of the graphics execution unit 508 of FIG. 5B.
[0126] The execution unit 600 also includes a compute unit 610 that
includes multiple different types of functional units. In one
embodiment the compute unit 610 includes an ALU unit 611 that
includes an array of arithmetic logic units. The ALU unit 611 can
be configured to perform 64-bit, 32-bit, and 16-bit integer and
floating point operations. Integer and floating point operations
may be performed simultaneously. The compute unit 610 can also
include a systolic array 612, and a math unit 613. The systolic
array 612 includes a W wide and D deep network of data processing
units that can be used to perform vector or other data-parallel
operations in a systolic manner. In one embodiment the systolic
array 612 can be configured to perform matrix operations, such as
matrix dot product operations. In one embodiment the systolic array
612 support 16-bit floating point operations, as well as 8-bit and
4-bit integer operations. In one embodiment the systolic array 612
can be configured to accelerate machine learning operations. In
such embodiments, the systolic array 612 can be configured with
support for the bfloat 16-bit floating point format. In one
embodiment, a math unit 613 can be included to perform a specific
subset of mathematical operations in an efficient and lower-power
manner than then ALU unit 611. The math unit 613 can include a
variant of math logic that may be found in shared function logic of
a graphics processing engine provided by other embodiments (e.g.,
math logic 422 of the shared function logic 420 of FIG. 4). In one
embodiment the math unit 613 can be configured to perform 32-bit
and 64-bit floating point operations.
[0127] The thread control unit 601 includes logic to control the
execution of threads within the execution unit. The thread control
unit 601 can include thread arbitration logic to start, stop, and
preempt execution of threads within the execution unit 600. The
thread state unit 602 can be used to store thread state for threads
assigned to execute on the execution unit 600. Storing the thread
state within the execution unit 600 enables the rapid pre-emption
of threads when those threads become blocked or idle. The
instruction fetch/prefetch unit 603 can fetch instructions from an
instruction cache of higher level execution logic (e.g.,
instruction cache 506 as in FIG. 5A). The instruction
fetch/prefetch unit 603 can also issue prefetch requests for
instructions to be loaded into the instruction cache based on an
analysis of currently executing threads. The instruction decode
unit 604 can be used to decode instructions to be executed by the
compute units. In one embodiment, the instruction decode unit 604
can be used as a secondary decoder to decode complex instructions
into constituent micro-operations.
[0128] The execution unit 600 additionally includes a register file
606 that can be used by hardware threads executing on the execution
unit 600. Registers in the register file 606 can be divided across
the logic used to execute multiple simultaneous threads within the
compute unit 610 of the execution unit 600. The number of logical
threads that may be executed by the graphics execution unit 600 is
not limited to the number of hardware threads, and multiple logical
threads can be assigned to each hardware thread. The size of the
register file 606 can vary across embodiments based on the number
of supported hardware threads. In one embodiment, register renaming
may be used to dynamically allocate registers to hardware
threads.
[0129] FIG. 7 is a block diagram illustrating a graphics processor
instruction formats 700 according to some embodiments. In one or
more embodiment, the graphics processor execution units support an
instruction set having instructions in multiple formats. The solid
lined boxes illustrate the components that are generally included
in an execution unit instruction, while the dashed lines include
components that are optional or that are only included in a sub-set
of the instructions. In some embodiments, instruction format 700
described and illustrated are macro-instructions, in that they are
instructions supplied to the execution unit, as opposed to
micro-operations resulting from instruction decode once the
instruction is processed.
[0130] In some embodiments, the graphics processor execution units
natively support instructions in a 128-bit instruction format 710.
A 64-bit compacted instruction format 730 is available for some
instructions based on the selected instruction, instruction
options, and number of operands. The native 128-bit instruction
format 710 provides access to all instruction options, while some
options and operations are restricted in the 64-bit format 730. The
native instructions available in the 64-bit format 730 vary by
embodiment. In some embodiments, the instruction is compacted in
part using a set of index values in an index field 713. The
execution unit hardware references a set of compaction tables based
on the index values and uses the compaction table outputs to
reconstruct a native instruction in the 128-bit instruction format
710. Other sizes and formats of instruction can be used.
[0131] For each format, instruction opcode 712 defines the
operation that the execution unit is to perform. The execution
units execute each instruction in parallel across the multiple data
elements of each operand. For example, in response to an add
instruction the execution unit performs a simultaneous add
operation across each color channel representing a texture element
or picture element. By default, the execution unit performs each
instruction across all data channels of the operands. In some
embodiments, instruction control field 714 enables control over
certain execution options, such as channels selection (e.g.,
predication) and data channel order (e.g., swizzle). For
instructions in the 128-bit instruction format 710 an exec-size
field 716 limits the number of data channels that will be executed
in parallel. In some embodiments, exec-size field 716 is not
available for use in the 64-bit compact instruction format 730.
[0132] Some execution unit instructions have up to three operands
including two source operands, src0 720, src1 722, and one
destination 718. In some embodiments, the execution units support
dual destination instructions, where one of the destinations is
implied. Data manipulation instructions can have a third source
operand (e.g., SRC2 724), where the instruction opcode 712
determines the number of source operands. An instruction's last
source operand can be an immediate (e.g., hard-coded) value passed
with the instruction.
[0133] In some embodiments, the 128-bit instruction format 710
includes an access/address mode field 726 specifying, for example,
whether direct register addressing mode or indirect register
addressing mode is used. When direct register addressing mode is
used, the register address of one or more operands is directly
provided by bits in the instruction.
[0134] In some embodiments, the 128-bit instruction format 710
includes an access/address mode field 726, which specifies an
address mode and/or an access mode for the instruction. In one
embodiment the access mode is used to define a data access
alignment for the instruction. Some embodiments support access
modes including a 16-byte aligned access mode and a 1-byte aligned
access mode, where the byte alignment of the access mode determines
the access alignment of the instruction operands. For example, when
in a first mode, the instruction may use byte-aligned addressing
for source and destination operands and when in a second mode, the
instruction may use 16-byte-aligned addressing for all source and
destination operands.
[0135] In one embodiment, the address mode portion of the
access/address mode field 726 determines whether the instruction is
to use direct or indirect addressing. When direct register
addressing mode is used bits in the instruction directly provide
the register address of one or more operands. When indirect
register addressing mode is used, the register address of one or
more operands may be computed based on an address register value
and an address immediate field in the instruction.
[0136] In some embodiments instructions are grouped based on opcode
712 bit-fields to simplify Opcode decode 740. For an 8-bit opcode,
bits 4, 5, and 6 allow the execution unit to determine the type of
opcode. The precise opcode grouping shown is merely an example. In
some embodiments, a move and logic opcode group 742 includes data
movement and logic instructions (e.g., move (mov), compare (cmp)).
In some embodiments, move and logic group 742 shares the five most
significant bits (MSB), where move (mov) instructions are in the
form of 0000xxxxb and logic instructions are in the form of
0001xxxxb. A flow control instruction group 744 (e.g., call, jump
(jmp)) includes instructions in the form of 0010xxxxb (e.g., 0x20).
A miscellaneous instruction group 746 includes a mix of
instructions, including synchronization instructions (e.g., wait,
send) in the form of 0011xxxxb (e.g., 0x30). A parallel math
instruction group 748 includes component-wise arithmetic
instructions (e.g., add, multiply (mul)) in the form of 0100xxxxb
(e.g., 0x40). The parallel math group 748 performs the arithmetic
operations in parallel across data channels. The vector math group
750 includes arithmetic instructions (e.g., dp4) in the form of
0101xxxxb (e.g., 0x50). The vector math group performs arithmetic
such as dot product calculations on vector operands. The
illustrated opcode decode 740, in one embodiment, can be used to
determine which portion of an execution unit will be used to
execute a decoded instruction. For example, some instructions may
be designated as systolic instructions that will be performed by a
systolic array. Other instructions, such as ray-tracing
instructions (not shown) can be routed to a ray-tracing core or
ray-tracing logic within a slice or partition of execution
logic.
[0137] Graphics Pipeline
[0138] FIG. 8 is a block diagram of another embodiment of a
graphics processor 800. Elements of FIG. 8 having the same
reference numbers (or names) as the elements of any other figure
herein can operate or function in any manner similar to that
described elsewhere herein, but are not limited to such.
[0139] In some embodiments, graphics processor 800 includes a
geometry pipeline 820, a media pipeline 830, a display engine 840,
thread execution logic 850, and a render output pipeline 870. In
some embodiments, graphics processor 800 is a graphics processor
within a multi-core processing system that includes one or more
general-purpose processing cores. The graphics processor is
controlled by register writes to one or more control registers (not
shown) or via commands issued to graphics processor 800 via a ring
interconnect 802. In some embodiments, ring interconnect 802
couples graphics processor 800 to other processing components, such
as other graphics processors or general-purpose processors.
Commands from ring interconnect 802 are interpreted by a command
streamer 803, which supplies instructions to individual components
of the geometry pipeline 820 or the media pipeline 830.
[0140] In some embodiments, command streamer 803 directs the
operation of a vertex fetcher 805 that reads vertex data from
memory and executes vertex-processing commands provided by command
streamer 803. In some embodiments, vertex fetcher 805 provides
vertex data to a vertex shader 807, which performs coordinate space
transformation and lighting operations to each vertex. In some
embodiments, vertex fetcher 805 and vertex shader 807 execute
vertex-processing instructions by dispatching execution threads to
execution units 852A-852B via a thread dispatcher 831.
[0141] In some embodiments, execution units 852A-852B are an array
of vector processors having an instruction set for performing
graphics and media operations. In some embodiments, execution units
852A-852B have an attached L1 cache 851 that is specific for each
array or shared between the arrays. The cache can be configured as
a data cache, an instruction cache, or a single cache that is
partitioned to contain data and instructions in different
partitions.
[0142] In some embodiments, geometry pipeline 820 includes
tessellation components to perform hardware-accelerated
tessellation of 3D objects. In some embodiments, a programmable
hull shader 811 configures the tessellation operations. A
programmable domain shader 817 provides back-end evaluation of
tessellation output. A tessellator 813 operates at the direction of
hull shader 811 and contains special purpose logic to generate a
set of detailed geometric objects based on a coarse geometric model
that is provided as input to geometry pipeline 820. In some
embodiments, if tessellation is not used, tessellation components
(e.g., hull shader 811, tessellator 813, and domain shader 817) can
be bypassed.
[0143] In some embodiments, complete geometric objects can be
processed by a geometry shader 819 via one or more threads
dispatched to execution units 852A-852B, or can proceed directly to
the clipper 829. In some embodiments, the geometry shader operates
on entire geometric objects, rather than vertices or patches of
vertices as in previous stages of the graphics pipeline. If the
tessellation is disabled the geometry shader 819 receives input
from the vertex shader 807. In some embodiments, geometry shader
819 is programmable by a geometry shader program to perform
geometry tessellation if the tessellation units are disabled.
[0144] Before rasterization, a clipper 829 processes vertex data.
The clipper 829 may be a fixed function clipper or a programmable
clipper having clipping and geometry shader functions. In some
embodiments, a rasterizer and depth test component 873 in the
render output pipeline 870 dispatches pixel shaders to convert the
geometric objects into per pixel representations. In some
embodiments, pixel shader logic is included in thread execution
logic 850. In some embodiments, an application can bypass the
rasterizer and depth test component 873 and access un-rasterized
vertex data via a stream out unit 823.
[0145] The graphics processor 800 has an interconnect bus,
interconnect fabric, or some other interconnect mechanism that
allows data and message passing amongst the major components of the
processor. In some embodiments, execution units 852A-852B and
associated logic units (e.g., L1 cache 851, sampler 854, texture
cache 858, etc.) interconnect via a data port 856 to perform memory
access and communicate with render output pipeline components of
the processor. In some embodiments, sampler 854, caches 851, 858
and execution units 852A-852B each have separate memory access
paths. In one embodiment the texture cache 858 can also be
configured as a sampler cache.
[0146] In some embodiments, render output pipeline 870 contains a
rasterizer and depth test component 873 that converts vertex-based
objects into an associated pixel-based representation. In some
embodiments, the rasterizer logic includes a windower/masker unit
to perform fixed function triangle and line rasterization. An
associated render cache 878 and depth cache 879 are also available
in some embodiments. A pixel operations component 877 performs
pixel-based operations on the data, though in some instances, pixel
operations associated with 2D operations (e.g. bit block image
transfers with blending) are performed by the 2D engine 841, or
substituted at display time by the display controller 843 using
overlay display planes. In some embodiments, a shared L3 cache 875
is available to all graphics components, allowing the sharing of
data without the use of main system memory.
[0147] In some embodiments, graphics processor media pipeline 830
includes a media engine 837 and a video front-end 834. In some
embodiments, video front-end 834 receives pipeline commands from
the command streamer 803. In some embodiments, media pipeline 830
includes a separate command streamer. In some embodiments, video
front-end 834 processes media commands before sending the command
to the media engine 837. In some embodiments, media engine 837
includes thread spawning functionality to spawn threads for
dispatch to thread execution logic 850 via thread dispatcher
831.
[0148] In some embodiments, graphics processor 800 includes a
display engine 840. In some embodiments, display engine 840 is
external to processor 800 and couples with the graphics processor
via the ring interconnect 802, or some other interconnect bus or
fabric. In some embodiments, display engine 840 includes a 2D
engine 841 and a display controller 843. In some embodiments,
display engine 840 contains special purpose logic capable of
operating independently of the 3D pipeline. In some embodiments,
display controller 843 couples with a display device (not shown),
which may be a system integrated display device, as in a laptop
computer, or an external display device attached via a display
device connector.
[0149] In some embodiments, the geometry pipeline 820 and media
pipeline 830 are configurable to perform operations based on
multiple graphics and media programming interfaces and are not
specific to any one application programming interface (API). In
some embodiments, driver software for the graphics processor
translates API calls that are specific to a particular graphics or
media library into commands that can be processed by the graphics
processor. In some embodiments, support is provided for the Open
Graphics Library (OpenGL), Open Computing Language (OpenCL), and/or
Vulkan graphics and compute API, all from the Khronos Group. In
some embodiments, support may also be provided for the Direct3D
library from the Microsoft Corporation. In some embodiments, a
combination of these libraries may be supported. Support may also
be provided for the Open Source Computer Vision Library (OpenCV). A
future API with a compatible 3D pipeline would also be supported if
a mapping can be made from the pipeline of the future API to the
pipeline of the graphics processor.
[0150] Graphics Pipeline Programming
[0151] FIG. 9A is a block diagram illustrating a graphics processor
command format 900 according to some embodiments. FIG. 9B is a
block diagram illustrating a graphics processor command sequence
910 according to an embodiment. The solid lined boxes in FIG. 9A
illustrate the components that are generally included in a graphics
command while the dashed lines include components that are optional
or that are only included in a sub-set of the graphics commands.
The exemplary graphics processor command format 900 of FIG. 9A
includes data fields to identify a client 902, a command operation
code (opcode) 904, and data 906 for the command. A sub-opcode 905
and a command size 908 are also included in some commands.
[0152] In some embodiments, client 902 specifies the client unit of
the graphics device that processes the command data. In some
embodiments, a graphics processor command parser examines the
client field of each command to condition the further processing of
the command and route the command data to the appropriate client
unit. In some embodiments, the graphics processor client units
include a memory interface unit, a render unit, a 2D unit, a 3D
unit, and a media unit. Each client unit has a corresponding
processing pipeline that processes the commands. Once the command
is received by the client unit, the client unit reads the opcode
904 and, if present, sub-opcode 905 to determine the operation to
perform. The client unit performs the command using information in
data field 906. For some commands an explicit command size 908 is
expected to specify the size of the command. In some embodiments,
the command parser automatically determines the size of at least
some of the commands based on the command opcode. In some
embodiments commands are aligned via multiples of a double word.
Other command formats can be used.
[0153] The flow diagram in FIG. 9B illustrates an exemplary
graphics processor command sequence 910. In some embodiments,
software or firmware of a data processing system that features an
embodiment of a graphics processor uses a version of the command
sequence shown to set up, execute, and terminate a set of graphics
operations. A sample command sequence is shown and described for
purposes of example only as embodiments are not limited to these
specific commands or to this command sequence. Moreover, the
commands may be issued as batch of commands in a command sequence,
such that the graphics processor will process the sequence of
commands in at least partially concurrence.
[0154] In some embodiments, the graphics processor command sequence
910 may begin with a pipeline flush command 912 to cause any active
graphics pipeline to complete the currently pending commands for
the pipeline. In some embodiments, the 3D pipeline 922 and the
media pipeline 924 do not operate concurrently. The pipeline flush
is performed to cause the active graphics pipeline to complete any
pending commands. In response to a pipeline flush, the command
parser for the graphics processor will pause command processing
until the active drawing engines complete pending operations and
the relevant read caches are invalidated. Optionally, any data in
the render cache that is marked `dirty` can be flushed to memory.
In some embodiments, pipeline flush command 912 can be used for
pipeline synchronization or before placing the graphics processor
into a low power state.
[0155] In some embodiments, a pipeline select command 913 is used
when a command sequence requires the graphics processor to
explicitly switch between pipelines. In some embodiments, a
pipeline select command 913 is required only once within an
execution context before issuing pipeline commands unless the
context is to issue commands for both pipelines. In some
embodiments, a pipeline flush command 912 is required immediately
before a pipeline switch via the pipeline select command 913.
[0156] In some embodiments, a pipeline control command 914
configures a graphics pipeline for operation and is used to program
the 3D pipeline 922 and the media pipeline 924. In some
embodiments, pipeline control command 914 configures the pipeline
state for the active pipeline. In one embodiment, the pipeline
control command 914 is used for pipeline synchronization and to
clear data from one or more cache memories within the active
pipeline before processing a batch of commands.
[0157] In some embodiments, return buffer state commands 916 are
used to configure a set of return buffers for the respective
pipelines to write data. Some pipeline operations require the
allocation, selection, or configuration of one or more return
buffers into which the operations write intermediate data during
processing. In some embodiments, the graphics processor also uses
one or more return buffers to store output data and to perform
cross thread communication. In some embodiments, the return buffer
state 916 includes selecting the size and number of return buffers
to use for a set of pipeline operations.
[0158] The remaining commands in the command sequence differ based
on the active pipeline for operations. Based on a pipeline
determination 920, the command sequence is tailored to the 3D
pipeline 922 beginning with the 3D pipeline state 930 or the media
pipeline 924 beginning at the media pipeline state 940.
[0159] The commands to configure the 3D pipeline state 930 include
3D state setting commands for vertex buffer state, vertex element
state, constant color state, depth buffer state, and other state
variables that are to be configured before 3D primitive commands
are processed. The values of these commands are determined at least
in part based on the particular 3D API in use. In some embodiments,
3D pipeline state 930 commands are also able to selectively disable
or bypass certain pipeline elements if those elements will not be
used.
[0160] In some embodiments, 3D primitive 932 command is used to
submit 3D primitives to be processed by the 3D pipeline. Commands
and associated parameters that are passed to the graphics processor
via the 3D primitive 932 command are forwarded to the vertex fetch
function in the graphics pipeline. The vertex fetch function uses
the 3D primitive 932 command data to generate vertex data
structures. The vertex data structures are stored in one or more
return buffers. In some embodiments, 3D primitive 932 command is
used to perform vertex operations on 3D primitives via vertex
shaders. To process vertex shaders, 3D pipeline 922 dispatches
shader execution threads to graphics processor execution units.
[0161] In some embodiments, 3D pipeline 922 is triggered via an
execute 934 command or event. In some embodiments, a register write
triggers command execution. In some embodiments execution is
triggered via a `go` or `kick` command in the command sequence. In
one embodiment, command execution is triggered using a pipeline
synchronization command to flush the command sequence through the
graphics pipeline. The 3D pipeline will perform geometry processing
for the 3D primitives. Once operations are complete, the resulting
geometric objects are rasterized and the pixel engine colors the
resulting pixels. Additional commands to control pixel shading and
pixel back end operations may also be included for those
operations.
[0162] In some embodiments, the graphics processor command sequence
910 follows the media pipeline 924 path when performing media
operations. In general, the specific use and manner of programming
for the media pipeline 924 depends on the media or compute
operations to be performed. Specific media decode operations may be
offloaded to the media pipeline during media decode. In some
embodiments, the media pipeline can also be bypassed and media
decode can be performed in whole or in part using resources
provided by one or more general-purpose processing cores. In one
embodiment, the media pipeline also includes elements for
general-purpose graphics processor unit (GPGPU) operations, where
the graphics processor is used to perform SIMD vector operations
using computational shader programs that are not explicitly related
to the rendering of graphics primitives.
[0163] In some embodiments, media pipeline 924 is configured in a
similar manner as the 3D pipeline 922. A set of commands to
configure the media pipeline state 940 are dispatched or placed
into a command queue before the media object commands 942. In some
embodiments, commands for the media pipeline state 940 include data
to configure the media pipeline elements that will be used to
process the media objects. This includes data to configure the
video decode and video encode logic within the media pipeline, such
as encode or decode format. In some embodiments, commands for the
media pipeline state 940 also support the use of one or more
pointers to "indirect" state elements that contain a batch of state
settings.
[0164] In some embodiments, media object commands 942 supply
pointers to media objects for processing by the media pipeline. The
media objects include memory buffers containing video data to be
processed. In some embodiments, all media pipeline states must be
valid before issuing a media object command 942. Once the pipeline
state is configured and media object commands 942 are queued, the
media pipeline 924 is triggered via an execute command 944 or an
equivalent execute event (e.g., register write). Output from media
pipeline 924 may then be post processed by operations provided by
the 3D pipeline 922 or the media pipeline 924. In some embodiments,
GPGPU operations are configured and executed in a similar manner as
media operations.
[0165] Graphics Software Architecture
[0166] FIG. 10 illustrates an exemplary graphics software
architecture for a data processing system 1000 according to some
embodiments. In some embodiments, software architecture includes a
3D graphics application 1010, an operating system 1020, and at
least one processor 1030. In some embodiments, processor 1030
includes a graphics processor 1032 and one or more general-purpose
processor core(s) 1034. The graphics application 1010 and operating
system 1020 each execute in the system memory 1050 of the data
processing system.
[0167] In some embodiments, 3D graphics application 1010 contains
one or more shader programs including shader instructions 1012. The
shader language instructions may be in a high-level shader
language, such as the High-Level Shader Language (HLSL) of
Direct3D, the OpenGL Shader Language (GLSL), and so forth. The
application also includes executable instructions 1014 in a machine
language suitable for execution by the general-purpose processor
core 1034. The application also includes graphics objects 1016
defined by vertex data.
[0168] In some embodiments, operating system 1020 is a
Microsoft.RTM. Windows.RTM. operating system from the Microsoft
Corporation, a proprietary UNIX-like operating system, or an open
source UNIX-like operating system using a variant of the Linux
kernel. The operating system 1020 can support a graphics API 1022
such as the Direct3D API, the OpenGL API, or the Vulkan API. When
the Direct3D API is in use, the operating system 1020 uses a
front-end shader compiler 1024 to compile any shader instructions
1012 in HLSL into a lower-level shader language. The compilation
may be a just-in-time (JIT) compilation or the application can
perform shader pre-compilation. In some embodiments, high-level
shaders are compiled into low-level shaders during the compilation
of the 3D graphics application 1010. In some embodiments, the
shader instructions 1012 are provided in an intermediate form, such
as a version of the Standard Portable Intermediate Representation
(SPIR) used by the Vulkan API.
[0169] In some embodiments, user mode graphics driver 1026 contains
a back-end shader compiler 1027 to convert the shader instructions
1012 into a hardware specific representation. When the OpenGL API
is in use, shader instructions 1012 in the GLSL high-level language
are passed to a user mode graphics driver 1026 for compilation. In
some embodiments, user mode graphics driver 1026 uses operating
system kernel mode functions 1028 to communicate with a kernel mode
graphics driver 1029. In some embodiments, kernel mode graphics
driver 1029 communicates with graphics processor 1032 to dispatch
commands and instructions.
[0170] IP Core Implementations
[0171] One or more aspects of at least one embodiment may be
implemented by representative code stored on a machine-readable
medium which represents and/or defines logic within an integrated
circuit such as a processor. For example, the machine-readable
medium may include instructions which represent various logic
within the processor. When read by a machine, the instructions may
cause the machine to fabricate the logic to perform the techniques
described herein. Such representations, known as "IP cores," are
reusable units of logic for an integrated circuit that may be
stored on a tangible, machine-readable medium as a hardware model
that describes the structure of the integrated circuit. The
hardware model may be supplied to various customers or
manufacturing facilities, which load the hardware model on
fabrication machines that manufacture the integrated circuit. The
integrated circuit may be fabricated such that the circuit performs
operations described in association with any of the embodiments
described herein.
[0172] FIG. 11A is a block diagram illustrating an IP core
development system 1100 that may be used to manufacture an
integrated circuit to perform operations according to an
embodiment. The IP core development system 1100 may be used to
generate modular, re-usable designs that can be incorporated into a
larger design or used to construct an entire integrated circuit
(e.g., an SOC integrated circuit). A design facility 1130 can
generate a software simulation 1110 of an IP core design in a
high-level programming language (e.g., C/C++). The software
simulation 1110 can be used to design, test, and verify the
behavior of the IP core using a simulation model 1112. The
simulation model 1112 may include functional, behavioral, and/or
timing simulations. A register transfer level (RTL) design 1115 can
then be created or synthesized from the simulation model 1112. The
RTL design 1115 is an abstraction of the behavior of the integrated
circuit that models the flow of digital signals between hardware
registers, including the associated logic performed using the
modeled digital signals. In addition to an RTL design 1115,
lower-level designs at the logic level or transistor level may also
be created, designed, or synthesized. Thus, the particular details
of the initial design and simulation may vary.
[0173] The RTL design 1115 or equivalent may be further synthesized
by the design facility into a hardware model 1120, which may be in
a hardware description language (HDL), or some other representation
of physical design data. The HDL may be further simulated or tested
to verify the IP core design. The IP core design can be stored for
delivery to a 3.sup.rd party fabrication facility 1165 using
non-volatile memory 1140 (e.g., hard disk, flash memory, or any
non-volatile storage medium). Alternatively, the IP core design may
be transmitted (e.g., via the Internet) over a wired connection
1150 or wireless connection 1160. The fabrication facility 1165 may
then fabricate an integrated circuit that is based at least in part
on the IP core design. The fabricated integrated circuit can be
configured to perform operations in accordance with at least one
embodiment described herein.
[0174] FIG. 11B illustrates a cross-section side view of an
integrated circuit package assembly 1170, according to some
embodiments described herein. The integrated circuit package
assembly 1170 illustrates an implementation of one or more
processor or accelerator devices as described herein. The package
assembly 1170 includes multiple units of hardware logic 1172, 1174
connected to a substrate 1180. The logic 1172, 1174 may be
implemented at least partly in configurable logic or
fixed-functionality logic hardware, and can include one or more
portions of any of the processor core(s), graphics processor(s), or
other accelerator devices described herein. Each unit of logic
1172, 1174 can be implemented within a semiconductor die and
coupled with the substrate 1180 via an interconnect structure 1173.
The interconnect structure 1173 may be configured to route
electrical signals between the logic 1172, 1174 and the substrate
1180, and can include interconnects such as, but not limited to
bumps or pillars. In some embodiments, the interconnect structure
1173 may be configured to route electrical signals such as, for
example, input/output (I/O) signals and/or power or ground signals
associated with the operation of the logic 1172, 1174. In some
embodiments, the substrate 1180 is an epoxy-based laminate
substrate. The package substrate 1180 may include other suitable
types of substrates in other embodiments. The package assembly 1170
can be connected to other electrical devices via a package
interconnect 1183. The package interconnect 1183 may be coupled to
a surface of the substrate 1180 to route electrical signals to
other electrical devices, such as a motherboard, other chipset, or
multi-chip module.
[0175] In some embodiments, the units of logic 1172, 1174 are
electrically coupled with a bridge 1182 that is configured to route
electrical signals between the logic 1172, 1174. The bridge 1182
may be a dense interconnect structure that provides a route for
electrical signals. The bridge 1182 may include a bridge substrate
composed of glass or a suitable semiconductor material. Electrical
routing features can be formed on the bridge substrate to provide a
chip-to-chip connection between the logic 1172, 1174.
[0176] Although two units of logic 1172, 1174 and a bridge 1182 are
illustrated, embodiments described herein may include more or fewer
logic units on one or more dies. The one or more dies may be
connected by zero or more bridges, as the bridge 1182 may be
excluded when the logic is included on a single die. Alternatively,
multiple dies or units of logic can be connected by one or more
bridges. Additionally, multiple logic units, dies, and bridges can
be connected together in other possible configurations, including
three-dimensional configurations.
[0177] FIG. 11C illustrates a package assembly 1190 that includes
multiple units of hardware logic chiplets connected to a substrate
1180 (e.g., base die). A graphics processing unit, parallel
processor, and/or compute accelerator as described herein can be
composed from diverse silicon chiplets that are separately
manufactured. In this context, a chiplet is an at least partially
packaged integrated circuit that includes distinct units of logic
that can be assembled with other chiplets into a larger package. A
diverse set of chiplets with different IP core logic can be
assembled into a single device. Additionally the chiplets can be
integrated into a base die or base chiplet using active interposer
technology. The concepts described herein enable the
interconnection and communication between the different forms of IP
within the GPU. IP cores can be manufactured using different
process technologies and composed during manufacturing, which
avoids the complexity of converging multiple IPs, especially on a
large SoC with several flavors IPs, to the same manufacturing
process. Enabling the use of multiple process technologies improves
the time to market and provides a cost-effective way to create
multiple product SKUs. Additionally, the disaggregated IPs are more
amenable to being power gated independently, components that are
not in use on a given workload can be powered off, reducing overall
power consumption.
[0178] The hardware logic chiplets can include special purpose
hardware logic chiplets 1172, logic or I/O chiplets 1174, and/or
memory chiplets 1175. The hardware logic chiplets 1172 and logic or
I/O chiplets 1174 may be implemented at least partly in
configurable logic or fixed-functionality logic hardware and can
include one or more portions of any of the processor core(s),
graphics processor(s), parallel processors, or other accelerator
devices described herein. The memory chiplets 1175 can be DRAM
(e.g., GDDR, HBM) memory or cache (SRAM) memory.
[0179] Each chiplet can be fabricated as separate semiconductor die
and coupled with the substrate 1180 via an interconnect structure
1173. The interconnect structure 1173 may be configured to route
electrical signals between the various chiplets and logic within
the substrate 1180. The interconnect structure 1173 can include
interconnects such as, but not limited to bumps or pillars. In some
embodiments, the interconnect structure 1173 may be configured to
route electrical signals such as, for example, input/output (I/O)
signals and/or power or ground signals associated with the
operation of the logic, I/O and memory chiplets.
[0180] In some embodiments, the substrate 1180 is an epoxy-based
laminate substrate. The substrate 1180 may include other suitable
types of substrates in other embodiments. The package assembly 1190
can be connected to other electrical devices via a package
interconnect 1183. The package interconnect 1183 may be coupled to
a surface of the substrate 1180 to route electrical signals to
other electrical devices, such as a motherboard, other chipset, or
multi-chip module.
[0181] In some embodiments, a logic or I/O chiplet 1174 and a
memory chiplet 1175 can be electrically coupled via a bridge 1187
that is configured to route electrical signals between the logic or
I/O chiplet 1174 and a memory chiplet 1175. The bridge 1187 may be
a dense interconnect structure that provides a route for electrical
signals. The bridge 1187 may include a bridge substrate composed of
glass or a suitable semiconductor material. Electrical routing
features can be formed on the bridge substrate to provide a
chip-to-chip connection between the logic or I/O chiplet 1174 and a
memory chiplet 1175. The bridge 1187 may also be referred to as a
silicon bridge or an interconnect bridge. For example, the bridge
1187, in some embodiments, is an Embedded Multi-die Interconnect
Bridge (EMIB). In some embodiments, the bridge 1187 may simply be a
direct connection from one chiplet to another chiplet.
[0182] The substrate 1180 can include hardware components for I/O
1191, cache memory 1192, and other hardware logic 1193. A fabric
1185 can be embedded in the substrate 1180 to enable communication
between the various logic chiplets and the logic 1191, 1193 within
the substrate 1180. In one embodiment, the I/O 1191, fabric 1185,
cache, bridge, and other hardware logic 1193 can be integrated into
a base die that is layered on top of the substrate 1180.
[0183] In various embodiments a package assembly 1190 can include
fewer or greater number of components and chiplets that are
interconnected by a fabric 1185 or one or more bridges 1187. The
chiplets within the package assembly 1190 may be arranged in a 3D
or 2.5D arrangement. In general, bridge structures 1187 may be used
to facilitate a point to point interconnect between, for example,
logic or I/O chiplets and memory chiplets. The fabric 1185 can be
used to interconnect the various logic and/or I/O chiplets (e.g.,
chiplets 1172, 1174, 1191, 1193), with other logic and/or I/O
chiplets. In one embodiment, the cache memory 1192 within the
substrate can act as a global cache for the package assembly 1190,
part of a distributed global cache, or as a dedicated cache for the
fabric 1185.
[0184] FIG. 11D illustrates a package assembly 1194 including
interchangeable chiplets 1195, according to an embodiment. The
interchangeable chiplets 1195 can be assembled into standardized
slots on one or more base chiplets 1196, 1198. The base chiplets
1196, 1198 can be coupled via a bridge interconnect 1197, which can
be similar to the other bridge interconnects described herein and
may be, for example, an EMIB. Memory chiplets can also be connected
to logic or I/O chiplets via a bridge interconnect. I/O and logic
chiplets can communicate via an interconnect fabric. The base
chiplets can each support one or more slots in a standardized
format for one of logic or I/O or memory/cache.
[0185] In one embodiment, SRAM and power delivery circuits can be
fabricated into one or more of the base chiplets 1196, 1198, which
can be fabricated using a different process technology relative to
the interchangeable chiplets 1195 that are stacked on top of the
base chiplets. For example, the base chiplets 1196, 1198 can be
fabricated using a larger process technology, while the
interchangeable chiplets can be manufactured using a smaller
process technology. One or more of the interchangeable chiplets
1195 may be memory (e.g., DRAM) chiplets. Different memory
densities can be selected for the package assembly 1194 cased on
the power, and/or performance targeted for the product that uses
the package assembly 1194. Additionally, logic chiplets with a
different number of type of functional units can be selected at
time of assembly based on the power, and/or performance targeted
for the product. Additionally, chiplets containing IP logic cores
of differing types can be inserted into the interchangeable chiplet
slots, enabling hybrid processor designs that can mix and match
different technology IP blocks.
[0186] Exemplary System on a Chip Integrated Circuit
[0187] FIG. 12 illustrates an exemplary integrated circuit and
FIGS. 13A-13B illustrate associated graphics processors that may be
fabricated using one or more IP cores, according to various
embodiments described herein. In addition to what is illustrated,
other logic and circuits may be included, including additional
graphics processors/cores, peripheral interface controllers, or
general-purpose processor cores.
[0188] FIG. 12 is a block diagram illustrating an exemplary system
on a chip integrated circuit 1200 that may be fabricated using one
or more IP cores, according to an embodiment. Exemplary integrated
circuit 1200 includes one or more application processor(s) 1205
(e.g., CPUs), at least one graphics processor 1210, and may
additionally include an image processor 1215 and/or a video
processor 1220, any of which may be a modular IP core from the same
or multiple different design facilities. Integrated circuit 1200
includes peripheral or bus logic including a USB controller 1225,
UART controller 1230, an SPI/SDIO controller 1235, and an
I.sup.2S/I.sup.2C controller 1240. Additionally, the integrated
circuit can include a display device 1245 coupled to one or more of
a high-definition multimedia interface (HDMI) controller 1250 and a
mobile industry processor interface (MIPI) display interface 1255.
Storage may be provided by a flash memory subsystem 1260 including
flash memory and a flash memory controller. Memory interface may be
provided via a memory controller 1265 for access to SDRAM or SRAM
memory devices. Some integrated circuits additionally include an
embedded security engine 1270.
[0189] FIGS. 13A-13B are block diagrams illustrating exemplary
graphics processors for use within an SoC, according to embodiments
described herein. FIG. 13A illustrates an exemplary graphics
processor 1310 of a system on a chip integrated circuit that may be
fabricated using one or more IP cores, according to an embodiment.
FIG. 13B illustrates an additional exemplary graphics processor
1340 of a system on a chip integrated circuit that may be
fabricated using one or more IP cores, according to an embodiment.
Graphics processor 1310 of FIG. 13A is an example of a low power
graphics processor core. Graphics processor 1340 of FIG. 13B is an
example of a higher performance graphics processor core. Each of
the graphics processors 1310, 1340 can be variants of the graphics
processor 1210 of FIG. 12.
[0190] As shown in FIG. 13A, graphics processor 1310 includes a
vertex processor 1305 and one or more fragment processor(s)
1315A-1315N (e.g., 1315A, 1315B, 1315C, 1315D, through 1315N-1, and
1315N). Graphics processor 1310 can execute different shader
programs via separate logic, such that the vertex processor 1305 is
optimized to execute operations for vertex shader programs, while
the one or more fragment processor(s) 1315A-1315N execute fragment
(e.g., pixel) shading operations for fragment or pixel shader
programs. The vertex processor 1305 performs the vertex processing
stage of the 3D graphics pipeline and generates primitives and
vertex data. The fragment processor(s) 1315A-1315N use the
primitive and vertex data generated by the vertex processor 1305 to
produce a framebuffer that is displayed on a display device. In one
embodiment, the fragment processor(s) 1315A-1315N are optimized to
execute fragment shader programs as provided for in the OpenGL API,
which may be used to perform similar operations as a pixel shader
program as provided for in the Direct 3D API.
[0191] Graphics processor 1310 additionally includes one or more
memory management units (MMUs) 1320A-1320B, cache(s) 1325A-1325B,
and circuit interconnect(s) 1330A-1330B. The one or more MMU(s)
1320A-1320B provide for virtual to physical address mapping for the
graphics processor 1310, including for the vertex processor 1305
and/or fragment processor(s) 1315A-1315N, which may reference
vertex or image/texture data stored in memory, in addition to
vertex or image/texture data stored in the one or more cache(s)
1325A-1325B. In one embodiment the one or more MMU(s) 1320A-1320B
may be synchronized with other MMUs within the system, including
one or more MMUs associated with the one or more application
processor(s) 1205, image processor 1215, and/or video processor
1220 of FIG. 12, such that each processor 1205-1220 can participate
in a shared or unified virtual memory system. The one or more
circuit interconnect(s) 1330A-1330B enable graphics processor 1310
to interface with other IP cores within the SoC, either via an
internal bus of the SoC or via a direct connection, according to
embodiments.
[0192] As shown FIG. 13B, graphics processor 1340 includes the one
or more MMU(s) 1320A-1320B, caches 1325A-1325B, and circuit
interconnects 1330A-1330B of the graphics processor 1310 of FIG.
13A. Graphics processor 1340 includes one or more shader core(s)
1355A-1355N (e.g., 1455A, 1355B, 1355C, 1355D, 1355E, 1355F,
through 1355N-1, and 1355N), which provides for a unified shader
core architecture in which a single core or type or core can
execute all types of programmable shader code, including shader
program code to implement vertex shaders, fragment shaders, and/or
compute shaders. The exact number of shader cores present can vary
among embodiments and implementations. Additionally, graphics
processor 1340 includes an inter-core task manager 1345, which acts
as a thread dispatcher to dispatch execution threads to one or more
shader cores 1355A-1355N and a tiling unit 1358 to accelerate
tiling operations for tile-based rendering, in which rendering
operations for a scene are subdivided in image space, for example
to exploit local spatial coherence within a scene or to optimize
use of internal caches.
[0193] Graphics Processing Units (GPUs) provide an excellent
environment for massively parallel execution of computer programs.
This execution is often in the form of graphics processing.
However, more and more often, the same hardware is being used to
process non-graphics-based programs, commonly referred to as GPGPU
(General Purpose GPU) or, more simply, Compute.
[0194] In both cases, Graphics and Compute, understanding the
factors that are influencing execution performance in a GPU
environment can be a challenge. In particular, both environments
involve executing a common computer program--often referred to as a
shader or a kernel--thousands to millions of times across an array
of available EUs (Execution Units).
[0195] CPU environments that are not massively parallel almost
always have a rich set of tools for understanding performance.
Sophisticated profiling algorithms--either instrumentation or
sample based--provide a wealth of information about how the
programs of interest are executing on the CPU hardware and where,
precisely, the programs are spending their time. This type of
infrastructure and information is essential to the task of crafting
software that is finely tuned for particular hardware.
[0196] GPU performance, on the other hand, has proven itself to be
a much more difficult problem to solve. One of the benefits of the
massive parallelism includes being able to hide significant
latency, and hence, latency in this environment can be handled to
minimize impact on performance. Moreover, with concurrent execution
being spread out over such a large amount of hardware, meaningfully
profiling code is much more difficult than it is in a CPU
environment.
[0197] As a result, measurement of GPU performance historically has
focused on high-level, aggregate indications of throughput and
system busyness. While these indicators are a necessary and
valuable component of GPU performance analysis, these indicators
often fail to provide the programmer with clear insight into how to
focus efforts intended to improve the kernels in question.
[0198] Complicating matters, as Compute becomes more and more
popular, the kernels involved are evolving to become more and more
complex. As a result, this lack of infrastructure to provide
kernel-level insights into GPU execution is becoming a significant
problem.
[0199] Kernel instrumentation is a prior approach for kernel-level
insights during GPU execution. For this prior approach, additional,
instrumentation only, code is inserted into the kernel at different
locations. Then, when the kernel is executed, this additional code
can record the latency between two instrumented points. This
quantification of execution latency can be used to determine where
execution time is being consumed and whether or not particular
sections of code are executing in line with expectations.
[0200] While this kernel instrumentation provides a useful
capability, it is also true that there are numerous disadvantages
to using this approach for the task of trying to understand kernel
performance. These disadvantages include system disturbance,
minimal kernel coverage, and lack of knowledge for determining a
location for instrumentation.
[0201] Much like the issues involved with the Heisenberg
Uncertainty Principle, using kernel instrumentation to measure
kernel execution latency is problematic because the instrumentation
used for the measurement is itself code that is embedded into the
kernel code that the developer is trying to understand. Moreover,
the code is intrusive and, hence, the mere presence of the code has
the potential--and likelihood--of changing the behavior of the
kernel in a substantive way. This statement is especially true in
cases where the kernel's use of resources, e.g. registers, is near
a boundary and the addition of the instrumentation results in the
kernel crossing over that boundary.
[0202] An ideal solution to the problem of profiling kernel
execution provides information about all aspects of the kernel. The
kernel instrumentation falls considerably short of this ideal
because it provides execution latency information not for the
entire kernel, but rather only a small number of instrumented
points.
[0203] Another problematic aspect of kernel instrumentation is
requiring the user to identify the points of interest a priori.
This requirement forces the user to make assumptions about where
the performance problems exist in the kernel. Under some
circumstances, users may have good insight into where to look for
the problems. Under other circumstances, however, this assumption
is not valid. Indeed, one of the most valuable benefits of a
profiler type infrastructure is when it provides insights that
contradict user intuition, which actually happens frequently.
Finding these types of problems with kernel instrumentation would
be a labor-intensive, nonintuitive process.
[0204] The present design includes hardware to periodically sample
a stall state of each processing resource (e.g., processing unit,
processing engine, execution resource, execution unit (EU) 508A-N,
509A-N, 600, 852A-B, stream processors, streaming multiprocessor
(SM), graphics multiprocessors 1925, 1950, multi-core groups
1965A-1965N, compute unit, compute unit of graphics core next) in
the GPU; moreover, each sample is resolved down to one of a small
number of possible stall reasons (e.g., Instruction Dependency,
Pipeline Stall, Send Stall, etc.) The present design can be
expanded to include any aspect of processing resource operation
including stalls, instruction types, pipeline utilization, thread
utilization, and shader activity. The processing resource may
execute instructions and the processing resource may include ALUs,
FPUs, load/store units, branch prediction, and SIMD. These units
perform the operations or calculations in a graphics processor or
graphics environment. In one example, a processing resource
includes multiple ALUs grouped in one or more SIMD. A plurality of
compute units can be grouped to form a shader engine.
[0205] The present design also includes on-chip aggregation and
tracking of the association between instruction pointer and these
resolved stall reasons. This tracking is accomplished in a
cache-like structure where the address is the Instruction Pointer,
and the data is a concatenated count of the enumerated stall
reasons. Instruction Pointer "hits" result in a read-modify-write
increment of the relevant stall reason. Evicted data is written out
to a circular buffer in main memory that is periodically aggregated
into a full kernel-level view of the stall reasons.
[0206] Embodiments of this invention provide a key strategic
benefit--instruction-level kernel profiling for a GPU environment.
In particular, graphics developers have expressed an interest in
this capability, especially for new functionality such as Ray
Tracing. Yet, the most immediate application for kernel profiling
remains with developers of Compute workloads, many of whom are
operating in the AI and Machine Learning space.
[0207] The present design includes a novel on-chip, cache-like
aggregation structure. To quantify the intrusiveness of a profiling
infrastructure, one could measure the total bandwidth consumed
during the execution of a given workload two times. The baseline
measurement is taken with profiling disabled. The second
measurement is done with profiling enabled. If the
profiling-enabled run exhibits only a small increase in
bandwidth--e.g. less than, perhaps, 1% --then one can infer that
there is significant on-chip aggregation of the profiling data.
[0208] For processing resource Stall Sampling Basics, if more than
one stall issues in kernel, then this design can compare stalls
across all threads. For the purposes of hardware organization in
one example, the processing resources in an exemplary GPU can be
grouped (e.g., group execution units into a component known as a
Dual Subslice (DSS), 16 compute units into a shader engine, up to 4
shader engines into a GPU, 4 streaming multiprocessors to form a
SMM, etc.). For a typical design, each DSS will have eight
processing resources and there might be a total of 16 DSSs.
[0209] In one example, each processing resource generates data
(e.g., an 8 bit vector) and sends this data to a cache like
aggregate structure for a grouping of processing resources. The
processing resource stall sampling is designed such that each
grouping of processing resources has hardware to periodically
sample each of its processing resources in succession. The timing
of the sampling is set up such that, across the entire system, one
sample happens, on average, every Samplelnterval cycles. For
example, FIG. 14 shows an example execution circuitry 1400 for
groupings of processing resources in accordance with one
embodiment. In one embodiment one or more processing resource
(e.g., 0-7, 0-11) can be combined into a fused processing resource
(or DSS) 1420, 1422, 1424, 1426, 1430, 1432, 1434, and 1436 having
control logic 1412 that can be separate from each fused execution
unit or integrated with each fused processing resource or DSS as
illustrated with control logic 1412A-1412H. Each control logic has
sampling functionality for sampling each processing resource that
is associated with the control logic. The control logic may include
or be associated with a cache unit for storing information received
from an associated processing resource.
[0210] Each processing resource in the fused processing resource
group can be configured to execute a separate SIMD hardware thread.
The number of processing resources in a fused processing resource
group can vary according to embodiments. Additionally, various SIMD
widths can be performed per-processing resource (PR), including but
not limited to SIMD8, SIMD16, and SIMD32.
[0211] FIG. 14 illustrates 8 DSSs with 8 PRs per DSS. In one
example, this design configures execution to have 1 sample every
500 clocks (i.e., SampleInterval=500), it would take 8*8*500=32,000
clocks to sample the 64 PRs of the execution circuitry. Moreover,
this present design can interleave the sampling across DSSs to
cause a maximum amount of time between samples on each DSS. For
example, this present design might choose to sample in the
following order:
[0212] DSS 1410A, PR0
[0213] DSS 1410B, PR0
[0214] . . .
[0215] DSS 1410H, PR0
[0216] DSS 1410A, PR1
[0217] . . .
[0218] DSS 1410G, PR7
[0219] DSS 1410H, PR7
[0220] In this scenario, a given DSS only needs to sample one of
its PRs once every 8*500=4,000 cycles. Of course, these numbers are
highly dependent on the particulars of a given GPU's
organization.
[0221] When the logic on a given DSS (or fused PR group) indicates
it is time to sample the stall state for a particular PR, the
execution of the PR is inspected precisely on a chosen clock cycle.
If the PR is idle or executing an instruction, no information is
recorded. Note that an alternative implementation may choose to
record counts for these states as well. If, on the other hand,
there are threads allocated on the PR, but no instruction is
executing in the identified cycle, the PR is classified as
stalled.
[0222] At this point, the hardware (e.g., control logic
1412A-1412H) is responsible for resolving the stall condition into
one of a number of supported stall reasons. This task involves some
complexity since a given PR likely has multiple threads that are
active, yet the resolution needs to result in one and only one
stall reason.
[0223] The present design resolves this problem in this
implementation based on priority level. First, some stalls are more
important than other stalls and, hence, a priority has been
established. The stall reasons are considered in priority order.
Second, for the lowest priority (e.g., Instruction Dependency),
threads are considered in a Round Robin manner, i.e. if thread X is
selected for a given sample, consideration will start from thread
X+1 and wrap appropriately for the next sample (e.g., first time
sample thread 3, then next time thread 4).
[0224] FIG. 15 provides an illustration of execution circuitry and
PR stall reason resolution in accordance with one embodiment. Once
the PR stall reason is resolved, the corresponding instruction
pointer address and the reason, encoded into a small number of
bits, is sent to the cache-like aggregation structure (e.g., cache
unit 1450A-1450H) in the DSS (or fused PR group). The execution
circuitry 1500 includes dependency check and decode logic that
receives threads 0-6, performing decoding, dependency check, and
determines which threads are ready for execution. Threads with a
ready status are sent to an arbiter 1510 (e.g., extended math jump
(EMJ) arbiter) or an arbiter 1520 (FPU arbiter). As discussed
above, the stall reasons 1550-1553 are assigned a priority level
(e.g., 1-4) and these reasons are considered in priority order.
Stall reason 1550 relates to a thread being stalled at arbiter
1530. Stall reason 1551 relates to a send stall at an output of
arbiter 1520. Stall reason 1552 relates to a thread being stalled
at the input of arbiter 1520. Stall reason 1553 relates to a thread
being stalled due to having an instruction dependency.
[0225] In one example, if stall reason 1550 and 1553 are
determined, then the control logic will only record a highest
priority stall, in this case, stall reason 1550.
[0226] FIG. 16 illustrates a method 1600 having an exemplary
sequence of operations for processing a new sample in a cache-like
aggregation structure (e.g., cache units 1450A-1450H) in accordance
with one embodiment. A graphics processing unit, graphics
multiprocessor, or graphics processor having cache units for each
grouping of PRs performs operations 1600 in accordance with one
embodiment. This cache-like structure resembles a cache where the
PR Instruction Pointer is the address and the activity data may
include a concatenated list of activity counts, each represented by
a small number of bits, e.g., 8. The activity data may relate to
stalls and reason counts for stalling activity, instruction types,
pipeline utilization, thread utilization, and shader activity. The
instruction types can be categorized into different classes and
activity counts provided for each different class. A power analysis
may analyze a number of sources for the instructions. A pipeline
utilization analysis may count how many pipelines are active to
generate activity counts. A thread utilization analysis may count
how many threads are active to generate activity counts. A shader
activity analysis may determine what type of shaders (e.g., pixel
shaders, vertex shaders, compute shaders) are active to generate
activity counts.
[0227] At operation 1602, initially a cache unit has an idle state.
At operation 1604, the cache unit receives an instruction pointer
address and performs an instruction pointer lookup within this
cache unit. If this instruction pointer address misses (e.g., not
found in lookup table and seen for the first time), a build entry
is performed for a new "cache line" that is allocated for the cache
unit and the identified activity count is initialized to a count
(e.g., 1) while all other activity counts are initialized to a
different count (e.g., 0). When subsequent samples identify the
same PR instruction pointer address and the cache line is still
present in the cache unit causing a hit, dedicated logic performs a
read operation 1606, a modify operation 1608, and a write operation
1610. The count associated with the activity identified by the
sample is incremented at modify operation 1608.
[0228] The line associated with a given instruction pointer remains
in the cache unit until it is either evicted (e.g., capacity evict
1616, max value evict 1612) or sampling ends and the contents of
the structure are flushed to memory. Eviction can happen for one of
two reasons.
[0229] For a capacity eviction at operation 1616, if a new sample
presents an instruction pointer that is not present in the cache
unit and all available lines of the cache unit are occupied, a
capacity-eviction scenario occurs. In this case, an existing line
must be evicted in order to make room for the new instruction
pointer sample. Since capacity eviction is analogous to standard
cache design, all existing best-known methods (e.g., replacement
policy, least recently used, FIFO, LIFO, most recently used) can be
applied to this aspect of the cache operation.
[0230] For a Max-value eviction at operation 1612, this eviction
scenario is unique to the cache-like aggregation structure of the
present design. Since each activity count is represented by a small
number of bits, there is a maximum value after which the count
would wrap, and the sampled data would no longer be accurately
represented by the count. Therefore, when a given cache line has an
activity count that reaches the maximum representable value, the
line must be evicted at operation 1612.
[0231] Eviction in the case of the cache-like structure (cache
unit) is different than that of a normal cache in that the
instruction pointer address and its corresponding data are written
out to a circular buffer in main memory. Of course, for a normal
cache, a given entry corresponds to a precise address in memory. In
our case, however, the instruction pointer address is simply part
of the data and the entire cache line--instruction pointer address
and activity counts--are evicted to the next available entry in a
circular buffer of main memory.
[0232] FIG. 17 illustrates an example table of the cache-like
aggregation structure with sample data and activity counts (e.g.,
reason counts for stall reasons) in accordance with one embodiment.
The first row, with a scoreboard value of 254 and assuming 8 bit
values, will max-value evict if the scoreboard value is incremented
one more time.
[0233] A synch stall field relates to a stall or delay between
threads to reach a common point. An instruction fetch field relates
to an instruction fetch from memory that is stalled. A jump
execution field relates to a branch stall when an algorithm
branches out. A scoreboard field relates to a stall based on a data
dependency. A send stall field relates to a send bus bandwidth
limit for a PR. A pipe stall field relates to a stall within a
pipeline such as FPU stall or bank collision. An internal stall
field relates to a stall caused from a memory bank collision. Other
field is a catch all for other types of stalls.
[0234] As described above, each cache-like aggregation structure
evicts its data to a corresponding circular buffer in main memory.
Periodically, software comes through and processes the contents of
the circular buffer, merging each line into the high-level stall
reason counts maintained for each encountered PR instruction
pointer.
[0235] FIG. 18 shows an example table of contents of a main-memory
circular buffer in accordance with one embodiment. The highlighted
instruction pointer address value is duplicated on two separate
lines. This may happen frequently. Based on the data values, the
first occurrence (index=0) was a capacity eviction and the last
occurrence (index=3) was a max-value eviction since the other count
is at 255 (assuming the use of eight-bit counters).
[0236] In some embodiments, a graphics processing unit (GPU) is
communicatively coupled to host/processor cores to accelerate
graphics operations, machine-learning operations, pattern analysis
operations, and various general-purpose GPU (GPGPU) functions. The
GPU may be communicatively coupled to the host processor/cores over
a bus or another interconnect (e.g., a high-speed interconnect such
as PCIe or NVLink). In other embodiments, the GPU may be integrated
on the same package or chip as the cores and communicatively
coupled to the cores over an internal processor bus/interconnect
(i.e., internal to the package or chip). Regardless of the manner
in which the GPU is connected, the processor cores may allocate
work to the GPU in the form of sequences of commands/instructions
contained in a work descriptor. The GPU then uses dedicated
circuitry/logic for efficiently processing these
commands/instructions.
[0237] In the following description, numerous specific details are
set forth to provide a more thorough understanding. However, it
will be apparent to one of skill in the art that the embodiments
described herein may be practiced without one or more of these
specific details. In other instances, well-known features have not
been described to avoid obscuring the details of the present
embodiments.
[0238] FIG. 19A-19C illustrate additional graphics multiprocessors,
according to embodiments. FIG. 19A-19B illustrate graphics
multiprocessors 1925, 1950. FIG. 19C illustrates a graphics
processing unit (GPU) 1980 which includes dedicated sets of
graphics processing resources arranged into multi-core groups
1965A-1965N. The illustrated graphics multiprocessors 1925, 1950
and the multi-core groups 1965A-1965N can be streaming
multiprocessor (SM) capable of simultaneous execution of a large
number of execution threads.
[0239] FIG. 19A shows a graphics multiprocessor 1925 according to
an additional embodiment. The graphics multiprocessor 1925 includes
multiple additional instances of execution resource units. For
example, the graphics multiprocessor 1925 can include multiple
instances of the instruction unit 1932A-1932B, register file
1934A-1934B, and texture unit(s) 1944A-1944B. The graphics
multiprocessor 1925 also includes multiple sets of graphics or
compute execution units (e.g., GPGPU core 1936A-1936B, tensor core
1937A-1937B, ray-tracing core 1938A-1938B) and multiple sets of
load/store units 1940A-1940B. In one embodiment the execution
resource units have a common instruction cache 1930, texture and/or
data cache memory 1942, and shared memory 1946.
[0240] The various components can communicate via an interconnect
fabric 1927. In one embodiment the interconnect fabric 1927
includes one or more crossbar switches to enable communication
between the various components of the graphics multiprocessor 325.
In one embodiment the interconnect fabric 1927 is a separate,
high-speed network fabric layer upon which each component of the
graphics multiprocessor 1925 is stacked. The components of the
graphics multiprocessor 1925 communicate with remote components via
the interconnect fabric 1927. For example, the GPGPU cores
1936A-1936B, 1937A-1937B, and 1938A-1938B can each communicate with
shared memory 1946 via the interconnect fabric 1927. The
interconnect fabric 1927 can arbitrate communication within the
graphics multiprocessor 1925 to ensure a fair bandwidth allocation
between components.
[0241] FIG. 19B shows a graphics multiprocessor 1950 according to
an additional embodiment. The graphics processor includes multiple
sets of execution resources 1956A-1956D, where each set of
execution resource includes multiple instruction units, register
files, GPGPU cores, and load store units. The execution resources
1956A-1956D can work in concert with texture unit(s) 1960A-1960D
for texture operations, while sharing an instruction cache 1954,
and shared memory 1953. In one embodiment the execution resources
1956A-1956D can share an instruction cache 1954 and shared memory
1953, as well as multiple instances of a texture and/or data cache
memory 1958A-1958B. The various components can communicate via an
interconnect fabric 1952 similar to the interconnect fabric 1927 of
FIG. 19A.
[0242] Persons skilled in the art will understand that the
architecture described in 3A-3B are descriptive and not limiting as
to the scope of the present embodiments. Thus, the techniques
described herein may be implemented on any properly configured
processing unit, including, without limitation, one or more mobile
application processors, one or more desktop or server central
processing units (CPUs) including multi-core CPUs, one or more
parallel processing units, as well as one or more graphics
processors or special purpose processing units, without departure
from the scope of the embodiments described herein.
[0243] In some embodiments a parallel processor or GPGPU as
described herein is communicatively coupled to host/processor cores
to accelerate graphics operations, machine-learning operations,
pattern analysis operations, and various general purpose GPU
(GPGPU) functions. The GPU may be communicatively coupled to the
host processor/cores over a bus or other interconnect (e.g., a high
speed interconnect such as PCIe or NVLink). In other embodiments,
the GPU may be integrated on the same package or chip as the cores
and communicatively coupled to the cores over an internal processor
bus/interconnect (i.e., internal to the package or chip).
Regardless of the manner in which the GPU is connected, the
processor cores may allocate work to the GPU in the form of
sequences of commands/instructions contained in a work descriptor.
The GPU then uses dedicated circuitry/logic for efficiently
processing these commands/instructions.
[0244] FIG. 19C illustrates a graphics processing unit (GPU) 1980
which includes dedicated sets of graphics processing resources
arranged into multi-core groups 1965A-N. While the details of only
a single multi-core group 1965A are provided, it will be
appreciated that the other multi-core groups 1965B-1965N may be
equipped with the same or similar sets of graphics processing
resources.
[0245] As illustrated, a multi-core group 1965A may include a set
of graphics cores 1970, a set of tensor cores 1971, and a set of
ray tracing cores 1972. Each core may include execution circuitry
1400 having a grouping of processing resources. A
scheduler/dispatcher 1968 schedules and dispatches the graphics
threads for execution on the various cores 1970, 1971, 1972. The
scheduler/dispatcher 1968 may include execution circuitry 1500. A
set of register files 1969 store operand values used by the cores
1970, 1971, 1972 when executing the graphics threads. These may
include, for example, integer registers for storing integer values,
floating point registers for storing floating point values, vector
registers for storing packed data elements (integer and/or floating
point data elements) and tile registers for storing tensor/matrix
values. In one embodiment, the tile registers are implemented as
combined sets of vector registers.
[0246] One or more combined level 1 (L1) caches and shared memory
units 1973 store graphics data such as texture data, vertex data,
pixel data, ray data, bounding volume data, etc., locally within
each multi-core group 1965A. One or more texture units 1974 can
also be used to perform texturing operations, such as texture
mapping and sampling. A Level 2 (L2) cache 1975 shared by all or a
subset of the multi-core groups 1965A-1965N stores graphics data
and/or instructions for multiple concurrent graphics threads. As
illustrated, the L2 cache 1975 may be shared across a plurality of
multi-core groups 1965A-1965N. One or more memory controllers 1967
couple the GPU 1980 to a memory 1966 which may be a system memory
(e.g., DRAM) and/or a dedicated graphics memory (e.g., GDDR6
memory).
[0247] Input/output (I/O) circuitry 1963 couples the GPU 1980 to
one or more I/O devices 1962 such as digital signal processors
(DSPs), network controllers, or user input devices. An on-chip
interconnect may be used to couple the I/O devices 1962 to the GPU
1980 and memory 1966. One or more I/O memory management units
(IOMMUs) 1964 of the I/O circuitry 3195 couple the I/O devices 1962
directly to the system memory 1966. In one embodiment, the IOMMU
1964 manages multiple sets of page tables to map virtual addresses
to physical addresses in system memory 1966. In this embodiment,
the I/O devices 1962, CPU(s) 1961, and GPU(s) 1980 may share the
same virtual address space.
[0248] In one implementation, the IOMMU 1964 supports
virtualization. In this case, it may manage a first set of page
tables to map guest/graphics virtual addresses to guest/graphics
physical addresses and a second set of page tables to map the
guest/graphics physical addresses to system/host physical addresses
(e.g., within system memory 1966). The base addresses of each of
the first and second sets of page tables may be stored in control
registers and swapped out on a context switch (e.g., so that the
new context is provided with access to the relevant set of page
tables). While not illustrated in FIG. 19C, each of the cores 1970,
1971, 1972 and/or multi-core groups 1965A-1965N may include
translation lookaside buffers (TLBs) to cache guest virtual to
guest physical translations, guest physical to host physical
translations, and guest virtual to host physical translations.
[0249] In one embodiment, the CPUs 1961, GPUs 1980, and I/O devices
1962 are integrated on a single semiconductor chip and/or chip
package. The illustrated memory 1966 may be integrated on the same
chip or may be coupled to the memory controllers 1967 via an
off-chip interface. In one implementation, the memory 1966
comprises GDDR6 memory which shares the same virtual address space
as other physical system-level memories, although the underlying
principles of the invention are not limited to this specific
implementation.
[0250] In one embodiment, the tensor cores 1971 include a plurality
of execution units specifically designed to perform matrix
operations, which are the fundamental compute operation used to
perform deep learning operations. For example, simultaneous matrix
multiplication operations may be used for neural network training
and inferencing. The tensor cores 1971 may perform matrix
processing using a variety of operand precisions including single
precision floating-point (e.g., 32 bits), half-precision floating
point (e.g., 16 bits), integer words (16 bits), bytes (8 bits), and
half-bytes (4 bits). In one embodiment, a neural network
implementation extracts features of each rendered scene,
potentially combining details from multiple frames, to construct a
high-quality final image.
[0251] In deep learning implementations, parallel matrix
multiplication work may be scheduled for execution on the tensor
cores 1971. The training of neural networks, in particular,
requires a significant number of matrix dot product operations. In
order to process an inner-product formulation of an
N.times.N.times.N matrix multiply, the tensor cores 1971 may
include at least N dot-product processing elements. Before the
matrix multiply begins, one entire matrix is loaded into tile
registers and at least one column of a second matrix is loaded each
cycle for N cycles. Each cycle, there are N dot products that are
processed.
[0252] Matrix elements may be stored at different precisions
depending on the particular implementation, including 16-bit words,
8-bit bytes (e.g., INT8) and 4-bit half-bytes (e.g., INT4).
Different precision modes may be specified for the tensor cores
1971 to ensure that the most efficient precision is used for
different workloads (e.g., such as inferencing workloads which can
tolerate quantization to bytes and half-bytes).
[0253] In one embodiment, the ray tracing cores 1972 accelerate ray
tracing operations for both real-time ray tracing and non-real-time
ray tracing implementations. In particular, the ray tracing cores
1972 include ray traversal/intersection circuitry for performing
ray traversal using bounding volume hierarchies (BVHs) and
identifying intersections between rays and primitives enclosed
within the BVH volumes. The ray tracing cores 1972 may also include
circuitry for performing depth testing and culling (e.g., using a Z
buffer or similar arrangement). In one implementation, the ray
tracing cores 1972 perform traversal and intersection operations in
concert with the image denoising techniques described herein, at
least a portion of which may be executed on the tensor cores 1971.
For example, in one embodiment, the tensor cores 1971 implement a
deep learning neural network to perform denoising of frames
generated by the ray tracing cores 1972. However, the CPU(s) 1961,
graphics cores 1970, and/or ray tracing cores 1972 may also
implement all or a portion of the denoising and/or deep learning
algorithms.
[0254] In addition, as described above, a distributed approach to
denoising may be employed in which the GPU 1980 is in a computing
device coupled to other computing devices over a network or high
speed interconnect. In this embodiment, the interconnected
computing devices share neural network learning/training data to
improve the speed with which the overall system learns to perform
denoising for different types of image frames and/or different
graphics applications.
[0255] In one embodiment, the ray tracing cores 1972 process all
BVH traversal and ray-primitive intersections, saving the graphics
cores 1970 from being overloaded with thousands of instructions per
ray. In one embodiment, each ray tracing core 1972 includes a first
set of specialized circuitry for performing bounding box tests
(e.g., for traversal operations) and a second set of specialized
circuitry for performing the ray-triangle intersection tests (e.g.,
intersecting rays which have been traversed). Thus, in one
embodiment, the multi-core group 1965A can simply launch a ray
probe, and the ray tracing cores 1972 independently perform ray
traversal and intersection and return hit data (e.g., a hit, no
hit, multiple hits, etc.) to the thread context. The other cores
1970, 1971 are freed to perform other graphics or compute work
while the ray tracing cores 1972 perform the traversal and
intersection operations.
[0256] In one embodiment, each ray tracing core 1972 includes a
traversal unit to perform BVH testing operations and an
intersection unit which performs ray-primitive intersection tests.
The intersection unit generates a "hit", "no hit", or "multiple
hit" response, which it provides to the appropriate thread. During
the traversal and intersection operations, the execution resources
of the other cores (e.g., graphics cores 1970 and tensor cores
1971) are freed to perform other forms of graphics work.
[0257] In one particular embodiment described below, a hybrid
rasterization/ray tracing approach is used in which work is
distributed between the graphics cores 1970 and ray tracing cores
1972.
[0258] In one embodiment, the ray tracing cores 1972 (and/or other
cores 1970, 1971) include hardware support for a ray tracing
instruction set such as Microsoft's DirectX Ray Tracing (DXR) which
includes a DispatchRays command, as well as ray-generation,
closest-hit, any-hit, and miss shaders, which enable the assignment
of unique sets of shaders and textures for each object. Another ray
tracing platform which may be supported by the ray tracing cores
1972, graphics cores 1970 and tensor cores 1971 is Vulkan 1.1.85.
Note, however, that the underlying principles of the invention are
not limited to any particular ray tracing ISA.
[0259] In general, the various cores 1972, 1971, 1970 may support a
ray tracing instruction set that includes instructions/functions
for ray generation, closest hit, any hit, ray-primitive
intersection, per-primitive and hierarchical bounding box
construction, miss, visit, and exceptions. More specifically, one
embodiment includes ray tracing instructions to perform the
following functions:
[0260] Ray Generation--Ray generation instructions may be executed
for each pixel, sample, or other user-defined work assignment.
[0261] Closest Hit--A closest hit instruction may be executed to
locate the closest intersection point of a ray with primitives
within a scene.
[0262] Any Hit--An any hit instruction identifies multiple
intersections between a ray and primitives within a scene,
potentially to identify a new closest intersection point.
[0263] Intersection--An intersection instruction performs a
ray-primitive intersection test and outputs a result.
[0264] Per-primitive Bounding box Construction--This instruction
builds a bounding box around a given primitive or group of
primitives (e.g., when building a new BVH or other acceleration
data structure).
[0265] Miss--Indicates that a ray misses all geometry within a
scene, or specified region of a scene.
[0266] Visit--Indicates the children volumes a ray will
traverse.
[0267] Exceptions--Includes various types of exception handlers
(e.g., invoked for various error conditions).
[0268] Some embodiments pertain to Example 1 that includes a
graphics processor, comprising a grouping of processing resources
and control logic that is associated with the grouping of
processing resources. The control logic is configured to sample a
state of at least one processing resource of the grouping of
processing resources and to determine activity data from the state
with the activity data including at least one of stalls and reason
counts for stalling activity, instruction types, pipeline
utilization, thread utilization, or shader activity.
[0269] Example 2 includes the subject matter of Example 1, further
comprising a cache unit that is associated with the grouping of
processing resources, the cache unit to receive an instruction
pointer address and activity data including a stall reason for each
state of processing resources that are associated with the cache
unit.
[0270] Example 3 includes the subject matter of Examples 1-2,
wherein each sampling of a state is scheduled for a chosen clock
cycle and is minimally intrusive.
[0271] Example 4 includes the subject matter of Examples 1-3,
wherein the control logic is configured to store a state when
threads are allocated on an processing resource with no instruction
being executed for a chosen cycle that is sampled.
[0272] Example 5 includes the subject matter of Examples 1-4,
wherein the control logic is configured to discard a state for a
chosen cycle that is sampled if the processing resource is idle or
executing an instruction.
[0273] Example 6 includes the subject matter of Examples 1-5,
wherein the control logic is configured to interleave samplings of
states of processing resources among the grouping of processing
resources and other groupings of processing resources, to resolve
the states into one of a number of supported stall reasons, and to
prioritize the supported stall reasons based on a priority level of
the stall reasons.
[0274] Example 7 includes the subject matter of Examples 1-6,
wherein the supported stalls and reason counts for stalling
activity comprise a synch stall field for a stall or delay between
threads to reach a common point, an instruction fetch field for an
instruction fetch from memory that is stalled, a jump execution
field for a branch stall when an algorithm branches out, a
scoreboard field for a stall based on a data dependency, a send
stall field for a send bus bandwidth limit for an processing
resource, a pipe stall field for a stall within a pipeline, and an
internal stall field for a stall caused from a memory bank
collision.
[0275] Some embodiments pertain to Example 8 that includes a cache
structure, comprising logic to perform operations of the cache
structure and memory coupled to the logic. The memory to store
instruction pointer addresses and associated data fields to
indicate activity data from sampling of processing resources. The
logic is configured to receive an instruction pointer address and
activity data for each state of processing resources that are
associated with the cache structure.
[0276] Example 9 includes the subject matter of Example 8, wherein
the logic is configured to perform an instruction pointer address
lookup within the cache structure.
[0277] Example 10 includes the subject matter of Examples 8-9,
wherein the logic is configured to build an entry for a new cache
line when the instruction pointer lookup misses, to store the
instruction pointer address and the activity data in the new cache
line, to initialize the identified activity including a stall
reason to 1 while all other reason counts are initialized to 0.
[0278] Example 11 includes the subject matter of Examples 8-10,
wherein the logic is configured to determine if all available lines
of the cache structure are occupied and to perform a
capacity-eviction to evict an existing line if all available lines
of the cache structure are occupied.
[0279] Example 12 includes the subject matter of Examples 8-11,
wherein the logic is configured to determine a hit for instruction
pointer address lookup, to perform a read operation of a cache line
for the instruction pointer address, to perform a modify operation
to increment a count of the identified activity, and to perform a
write operation for the cache line.
[0280] Example 13 includes the subject matter of Examples 8-12,
wherein the logic is configured for a maximum value eviction when a
given cache line has an activity count that reaches a maximum
representable value and performs the maximum value eviction by
evicting the instruction pointer address and its corresponding data
to a circular buffer in main memory.
[0281] Some embodiments pertain to Example 14 that includes a
method for minimally intrusive profiling of a graphics processing
unit (GPU), comprising receiving, with a cache unit, an instruction
pointer address and activity data for each state of processing
resources that are associated with the cache unit and performing an
instruction pointer address lookup within the cache unit for the
received instruction pointer address and associated activity
data.
[0282] Example 15 includes the subject matter of Example 14,
further comprising building an entry for a new cache line when the
instruction pointer lookup misses.
[0283] Example 16 includes the subject matter of Examples 14-15,
further comprising storing the instruction pointer address and the
activity data in the new cache line; and initializing the
identified activity including a stall reason to 1 while all other
reason counts are initialized to 0.
[0284] Example 17 includes the subject matter of Examples 14-16,
further comprising determining if all available lines of the cache
structure are occupied and to perform a capacity-eviction to evict
an existing line if all available lines of the cache structure are
occupied.
[0285] Example 18 includes the subject matter of Examples 14-17,
further comprising determining a hit for instruction pointer
address lookup.
[0286] Example 19 includes the subject matter of Examples 14-18,
further comprising performing a read operation of a cache line for
the instruction pointer address, performing a modify operation to
increment a count of the identified activity for the instruction
pointer address; and performing a write operation for the cache
line.
[0287] Example 20 includes the subject matter of Examples 14-19,
further comprising performing a maximum value eviction when a given
cache line has an activity count that reaches a maximum
representable value, wherein performing the maximum value eviction
comprises evicting the instruction pointer address and its
corresponding data to a circular buffer in main memory.
[0288] Example 21 includes the subject matter of Examples 14-20,
wherein the activity data includes at least one of stalls and
reason counts for stalling activity, instruction types, pipeline
utilization, thread utilization, or shader activity.
[0289] The foregoing description and drawings are to be regarded in
an illustrative rather than a restrictive sense. Persons skilled in
the art will understand that various modifications and changes may
be made to the embodiments described herein without departing from
the broader spirit and scope of the invention as set forth in the
appended claims.
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