U.S. patent application number 14/876741 was filed with the patent office on 2016-03-17 for logical grouping of profile data.
This patent application is currently assigned to Microsoft Technology Licensing, LLC. The applicant listed for this patent is Cenk Ergan, Michael C. Fanning. Invention is credited to Cenk Ergan, Michael C. Fanning.
Application Number | 20160077828 14/876741 |
Document ID | / |
Family ID | 49061991 |
Filed Date | 2016-03-17 |
United States Patent
Application |
20160077828 |
Kind Code |
A1 |
Ergan; Cenk ; et
al. |
March 17, 2016 |
LOGICAL GROUPING OF PROFILE DATA
Abstract
Profile data can be ascribed to groups as a function of an
organizational scheme that defines groups and relationships between
groups. The groups can be abstractions over profile data that are
meaningful for computer program analysis. Subsequently, grouped
data can be disseminated, queried, and visualized in numerous ways
to further aid program analysis.
Inventors: |
Ergan; Cenk; (Bellevue,
WA) ; Fanning; Michael C.; (Redmond, WA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Ergan; Cenk
Fanning; Michael C. |
Bellevue
Redmond |
WA
WA |
US
US |
|
|
Assignee: |
Microsoft Technology Licensing,
LLC
Redmond
WA
|
Family ID: |
49061991 |
Appl. No.: |
14/876741 |
Filed: |
October 6, 2015 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
13430968 |
Mar 27, 2012 |
9183108 |
|
|
14876741 |
|
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|
|
Current U.S.
Class: |
717/158 |
Current CPC
Class: |
G06F 8/443 20130101;
G06F 11/323 20130101; G06F 8/70 20130101; G06F 11/3409 20130101;
G06F 11/3495 20130101; G06F 11/3082 20130101 |
International
Class: |
G06F 9/44 20060101
G06F009/44 |
Claims
1. A method, comprising: employing at least one processor
configured to execute computer-executable instructions stored in a
memory to perform the following acts: receiving computer-program
profile data; ascribing profile data to logical groups based on an
organizational scheme specified external to a profile system and
target program, and configured to define the logical groups and
relationships between the logical groups, wherein the logical
groups are abstractions that correspond to high-level program
functionality; and presenting, on a display, at least a subset of
the profile data in the logical groups.
2. The method of claim 1 further comprises aggregating profile data
of a group.
3. The method of claim 1 further comprises suspending the act of
ascribing the profile data to the logical groups during a context
switch event.
4. The method of claim 1 further comprises collecting the profile
data by way of sampling and instrumentation.
5. The method of claim 1 further comprises ascribing the profile
data to the logical groups based on a priority values.
6. The method of claim 1 further comprises returning at least a
subset of the profiling data in response to a query.
7. The method of claim 1 further comprises delivering the profile
data and the logical groups ascribed thereto to one or more
subscribers to the profile data.
8. The method of claim 1 further comprises automatically generating
at least a portion of the organizational scheme.
9. A system, comprising: a processor coupled to a memory, the
processor configured to execute the following computer-executable
components stored in the memory: a first component configured to
ascribe computer program profile data to groups as a function of an
organizational scheme specified external to a profile system and
target program, and configured to define groups and relationships
between groups, the groups are abstractions that correspond to a
high-level view of computer program functionality; and a second
component configured to generate a visualization of at least a
subset of the profile data in the groups.
10. The system of claim 9, the organizational scheme is encoded
within the computer program.
11. The system of claim 9, the organizational scheme is encoded
external to the computer program.
12. The system of claim 9 further comprises a third component
configured to acquire the profile data with a combination of
instrumentation and sampling.
13. The system of claim 9, the organizational scheme specifies a
group in terms of a set of one or more stacks.
14. The system of claim 9, the organizational scheme specifies a
group in terms of a set of one or more start/stop event pairs.
15. The system of claim 9 further comprises a third component
configured to process a query against the profile data.
16. The system of claim 9 further comprises a third component
configured to update at least a portion of the organizational
scheme automatically.
17. A computer-readable storage medium having instructions stored
thereon that enable at least one processor to perform the following
acts upon execution: receiving computer-program profile data;
ascribing profile data to logical groups based on an organizational
scheme specified external to a profile system and target program,
and configured to define the logical groups and relationships
between the logical groups, wherein the logical groups are
abstractions that correspond to high-level program functionality;
and presenting, on a display, at least a subset of the profile data
in the logical groups.
18. The computer-readable storage medium of claim 17 further
comprises processing queries over grouped profile data.
19. The computer-readable storage medium of claim 17 further
comprises delivering grouped profile data to one or more
subscribers.
20. The computer-readable storage medium of claim 17 further
comprises acquiring the computer-program profile data by way of a
combination of program sampling and instrumentation.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This application is a continuation of U.S. application Ser.
No. 13/430,968 filed Mar. 27, 2012, the entirety of which is
incorporated herein by reference.
BACKGROUND
[0002] Profiling enables examination of program behavior to focus
performance tuning. A profiler is an automated tool that produces a
profile of a program from information collected during program
execution. A profile captures behavioral characteristics of a
program. One or more portions of program can be identified from a
profile as candidates for optimization. For example, a profile
might indicate that an excessive amount of time is spent executing
a particular function. In other words, a profile aids understating
of program behavior to allow concentration of optimization efforts.
Profilers are often classified based on their methods of gathering
data, among other things.
[0003] There are two distinct types of approaches to gathering
profile data, namely instrumentation and sampling. In the
instrumentation approach, code is added to a program to collect
information during execution. Here, the added code is an instrument
that measures program behavior as the program executes. For
example, the frequency and duration of function calls can be
measured. In the sampling approach, an executing program is halted
periodically, using operating system functionality, and sampled to
determine the current state of execution. Accordingly, it can be
noticed that twenty-percent of the time the program is executing a
specific code point, for example. The sampling approach thus
provides a statistical approximation rather than exact data.
[0004] Profile data is often presented as a call tree (a.k.a., call
graph) that breaks down program execution. For example, a call tree
can show function execution paths that were traversed in a program.
The root node of the call tree can point to the entry point into
the program and each other node in the tree can identify a called
function as well as performance data such as execution time of the
called function. The edges between nodes can represent function
calls, and cycles can be indicative of recursive calls. The call
tree can be analyzed by a developer to identify program hotspots,
such as functions that occupy a large portion of execution time,
among other things.
SUMMARY
[0005] The following presents a simplified summary in order to
provide a basic understanding of some aspects of the disclosed
subject matter. This summary is not an extensive overview. It is
not intended to identify key/critical elements or to delineate the
scope of the claimed subject matter. Its sole purpose is to present
some concepts in a simplified form as a prelude to the more
detailed description that is presented later.
[0006] Briefly described, the subject disclosure pertains to
logical grouping of profile data. Profile data is ascribed to
groups as a function of an organizational scheme that defines
groups and relationships between groups. The groups can be
abstractions over the profile data that are meaningful for program
analysis. In other words, the groups can convey information about
high-level functions of a program or sub-systems thereof (e.g.,
opening a document, recalculating layout, rendering a window . . .
), among other things. Context switches can also be accounted for
such that profile data is precise with respect to execution time
and central processor unit (CPU) utilization, for example. In
addition, grouped data can be queried and visualized in various
ways to further facilitate program analysis.
[0007] To the accomplishment of the foregoing and related ends,
certain illustrative aspects of the claimed subject matter are
described herein in connection with the following description and
the annexed drawings. These aspects are indicative of various ways
in which the subject matter may be practiced, all of which are
intended to be within the scope of the claimed subject matter.
Other advantages and novel features may become apparent from the
following detailed description when considered in conjunction with
the drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] FIG. 1 is a block diagram of a program profile system.
[0009] FIG. 2 is a block diagram of a program profile system.
[0010] FIG. 3 is block diagram of a representative-data collection
component.
[0011] FIG. 4 illustrates data collection for start/stop event
pairs.
[0012] FIG. 5 illustrates data collection for stack samples.
[0013] FIG. 6 is an exemplary screenshot of a table visualization
of profile data.
[0014] FIG. 7 is an exemplary screenshot of graph visualization of
profile data.
[0015] FIG. 8 is a flow chart diagram of a method of program
profiling.
[0016] FIG. 9 is a flow chart diagram of a method of grouping
profiling data.
[0017] FIG. 10 is a schematic block diagram illustrating a suitable
operating environment for aspects of the subject disclosure.
DETAILED DESCRIPTION
[0018] Profile data is conventionally too copious and granular to
be useful in identifying and resolving programming issues. By way
of example, typical applications include thousands of functions
(with names of varying readability), and hundreds of thousands of
functions can be called in a single second. Obscurities can also be
an issue since profiling data can be closely associated with
machine and operating system details. Furthermore, modern hardware
and operating systems introduce new complexities for managing data
across central processing units (CPUs) and threads.
[0019] In view of the above, it is difficult to understand whether
a specific function, for example, is taking an unreasonable amount
of time as it can only be compared against all other functions or
compared to the execution that it provokes. This latter analysis
treats all calls provoked by a function as equivalent. That is,
there is no way to distinguish, for example, between a call that is
not under developer control, such as a supporting operating system,
runtime, application framework, or library, and a call that is
under developer control. This information is valuable in terms of
making a fix, bearing in mind that a developer cannot influence the
actual implementation of code on which it depends.
[0020] The conventional call tree view addresses the issue of
multiple distinct pieces of profiling data by creating an aggregate
view. However, the call tree view can also be problematic. By way
of example, and not limitation, code can easily initiate
asynchronous operations that when profiled have no obvious
connection to the code that initiated it. If these asynchronous
operations are distributed across many distinct callbacks, the
aggregated cost implied by initiating code can be obscured, because
no individual callback surfaces as a problem in its own right but
only when aggregated with other callbacks. Thus, it is recognized
that conventional profilers can aggregate data. However, such data
may only be useful if it is broken down into pieces that are more
meaningful. For example, the time-to-execute attributed to an
asynchronous callback might be more usefully understood when broken
down into the various transactions that were associated with the
callback.
[0021] Stated differently, two conditions can be problematic with
respect to understanding profile data. First, profiling data can be
segmented into numerous and distinct pieces, where a coarser view
is more useful. Second, data can be aggregated where a more
granular view is helpful. For instance, consider a common handler
that is associated with multiple transactions. Here, it might be
helpful to break apart execution costs of hitting that handler and
organizing them in some way. In another scenario, a particular
operation can exercise many different callbacks. In this case, it
might be useful to examine the distributed costs of the
operation.
[0022] Details below are generally directed toward logical grouping
of profile data. An arbitrary organizational scheme that defines
groups and relationships between groups can be overlaid on profile
data to enable a high-level understanding of the structure and
meaning of executed code, among other things. More particularly,
profile data can be ascribed to one or more groups based on an
organizational scheme, where the groups are abstractions that are
meaningful to human users that analyze a program. By way of
example, functions can be bucketed as being related to window
layout, rendering, or both. Subsequently, it can be easily
determined that a program is spending an exorbitant amount of time
laying out content (e.g., web/document content) based on the
execution time/count associated with a layout group. Additionally,
processing can account for changes in context, where one process is
switched out of a central processing unit so that another process
can run, for instance. Accordingly, data can be managed in a
precise way at low levels such that when it is rolled up to high
levels in meaningful groups the data is accurate. Subsequent to
collection and processing of profile data, resulting grouped data
can be disseminated, queried, and visualized in various ways to
further aid program analysis.
[0023] Various aspects of the subject disclosure are now described
in more detail with reference to the annexed drawings, wherein like
numerals refer to like or corresponding elements throughout. It
should be understood, however, that the drawings and detailed
description relating thereto are not intended to limit the claimed
subject matter to the particular form disclosed. Rather, the
intention is to cover all modifications, equivalents, and
alternatives falling within the spirit and scope of the claimed
subject matter.
[0024] Referring initially to FIG. 1, a program profile system 100
is illustrated. Among other things, the program profile system 100
enables a top-down view of program performance based on a
high-level understanding of the semantic purpose of code and/or its
identity within a broader system (e.g., owned by program, subsystem
of program, subsystem within dependent component . . . ), among
other things such as program personnel. This can be accomplished by
processing and analyzing profile data gathered by data collection
component 110.
[0025] The data collection component 110 is configured to acquire
profile data regarding program 112. Profile data can be any
arbitrary collected and/or computed data associated with execution
of a program. In one instance, such profile data can correspond to
a time to execute with respect to a particular function/operation.
In another instance, the profile data can correspond to non-time-to
execute data including occurrence or frequency of one or more
events and optionally a payload associated with the occurrence of
one or more events, where an event is a signal that an executing
program has hit a specific point in code, and a payload is data
associated with hitting the specific point in code. By way of
example, non-time-to-execute profile data can include bytes or
objects allocated, page refreshes, registry reads/writes, worker
threads spawned, or associated binary, among other things. The
program (a.k.a. computer program) 112 comprises a set of
instructions specified in a computer programming language that when
executed by a processor performs actions prescribed by the set of
instructions. The data collection component 110 can gather profile
data from an executing program utilizing a variety of techniques
including instrumentation and sampling. Work performed by the
program 112 can by captured by event/execution probes (e.g., event
tracing events or callbacks from instrumented code) or stack
samples collected by an operating system, for example. Data can be
collected that enables analysis in terms of time-to-execute (e.g.,
central processing unit utilization or literal cycles spent in
active execution), heap allocations, or other arbitrary information
that can be expressed in custom payloads of generated events.
[0026] A combination of instrumentation and sampling can be
utilized by the data collection component 110. Data collected by
each approach can augment the other approach. For example, data can
be combined/merged, averaged, crosschecked, and/or statistically
normalized, among other things. In this manner, advantages of both
approaches can be exploited and disadvantages can be offset. As an
example, both approaches can result in an observer effect, which
means the act of observing a program itself can influence the
program. Sampling typically does not result in a significant
observer effect, but results in less precise data than that
gathered by instrumentation. Accordingly, sampling data can be
supplemented with instrumentation data, where appropriate.
Additional mitigation against the observer effect can also be
provided by creating profiling buckets that overtly group (and
therefore exclude from other data) profiling data associated with
operating system code that executes during production of events
from instrumented code and stacks from code sampling. Further, a
lightweight instrumentation approach can also be enabled where a
stack is received with respect to instrumented probes/events.
Suppose a single event is enabled solely for bytes allocated (which
results in very little observer effect). If the "bytes allocated"
event is associated with a stack, the bytes allocated data can be
grouped according to stack-specified bucketing. Note, in this
example, there is no sampling literally enabled. Hence, run-time
events and/or collected stacks can be utilized to organize profile
data.
[0027] Group component 120 is configured to ascribe profile data
gathered utilizing the data collection component 110 to specific
groups, or buckets, as a function of organizational scheme 114. In
other words, profile data can be correlated and associated based on
a descriptive mechanism that defines groups and relationships
between groups. In one instance, groups can convey information
about high-level functions of a program or sub-systems thereof
(e.g., opening a document, recalculating layout, rendering a window
. . . ). In furtherance of the foregoing, the group component 120
can be configured to initialize data structures based on a given
organizational scheme 114 and populate groups with profile data.
Other implementations are also possible including, but not limited
to, tagging profiling data with group information. In any event,
the result of processing performed by the group component 120 is
grouped data, which can be housed in a local or remotely accessible
data store 140.
[0028] The organizational scheme 114 can define groupings in terms
of function names (e.g., full function name within a binary file
(e.g., executable)), for example. Groups can be expressed in
hierarchical relationship of parents and children. However, groups
can be constructed that are mutually exclusive of one another, for
example by defining them as sibling nodes. Groups can include
sub-groups, also called categories, as child nodes, for example.
Note, however, that unless otherwise explicitly noted, use of the
term "group" or "groups" is intended to include sub-groups, or
categories. Functions can be associated with groups at any level
and can appear in an arbitrary number of groups. In other words,
functions are not limited to being ascribed to a single group but
instead can appear in multiple groups at any level of granularity.
As well, function information can be used to aggregate data, for
example based on stack samples and/or an instrumented/event-based
collection. For example, a binary file name and the function name
can be utilized as bases for grouping. This can be helpful with
respect to distinguishing execution that spans multiple binary
files. Groups can also define events that do not explicitly provide
binary/function details, but that provide an event identifier,
which is available in the collected data. Binary/function
information in this case can be implied by the set of code
locations that raise a specified event. In at least some operating
systems, a stack will be available on generating an event to
distinguish amongst multiple code locations raising the event.
Further, note that a group hierarchy as provided herein can be
independent of function execution paths. The reason for this is
twofold. First, in the instrumented case, a notion of a stack can
be employed for grouping data that is entirely decoupled from the
actual execution stack. Second, in the sampling case, arbitrary
unique stacks can be associated with the same group.
[0029] Priorities can also be designated for individual groups to
assist in grouping data. More specifically, priority enables
breaking of a default rule of organization based on the most
current stack frame (e.g., literal code stack, virtual event
start/stop pair stack). Such a priority value can be expressed
relative to all other groups and used to assist in
grouping/bucketing decisions. If data is under consideration for a
group that has a lower specified priority than other candidate
groups that exist to hold the data, the data can be attributed to
an alternate group that has the highest explicit or implied
priority. Groups defined in hierarchical relationships can have an
implicit priority based on that hierarchy. If a parent "A" includes
a child "B," for example, "B" is regarded as a higher priority
group over "A." Accordingly, data collected with a stack "A::B"
will therefore be associated with group "B." However, if group "B"
is explicitly specified at a lower priority than group "A," then
data will be grouped in group "A."
[0030] Profile data can be correlated in various ways to permit
flexible querying at various scopes, among other things. As
examples, profiling data might extend across process boundaries or
across machines. In one instance, profile data can be grouped by
time in accordance with timestamps. At a more granular level, the
profile data can be grouped by central processing units or threads.
Further, a universal activity identifier could be used to track
inter-thread communication in useful ways, for instance by tracking
activity associated with a multi-threaded transaction.
[0031] In one instance, the organizational scheme 114 can be
embodied as a data file that can be authored, edited, and
maintained out-of-band from, or independent of, other processes.
Further, the organizational scheme 114 can be easily passed around.
This can allow code experts to capture a useful model of analyzing
profile data for a program of which they are an expert and
distribute this model to non-expert users. For example, an expert
in the inner workings of a web browser could generate an
organizational scheme and distribute the scheme to web application
developers to aid performance tuning.
[0032] In one particular implementation, the organizational scheme
114 can be expressed in XML (eXtensible Markup Language) thus
providing general readability. In this implementation, and as
employed with further description below, groups or subgroups are
referred to as tags or tagsets, where a tag denotes a group and a
tagset refers to set of groups. Examples of organizational schemes
specified in XML are provided later herein.
[0033] Scheme generation component 130 is configured to facilitate
generation of the organizational scheme 114. In one instance, a
human user can manually author the organizational scheme 114
optionally employing pattern matching or other filtering mechanisms
(e.g., regular expressions, use of wild-card characters . . . ) to
aid specification. The scheme generation component 130 can enable
automatic or semi-automatic (e.g., with user assistance) generation
of groups, for instance based on available information including
context information. By way of example, and not limitation, source
control history, which stores information regarding changes made to
a program, can be mined by the scheme generation component 130 and
used to automatically generate groups. For instance, a "code owner"
group could be created to enable profile data to be broken down by
team or individual owner of code. This allows users to identify
experts who could assist with a performance problem easily.
[0034] Query processor component 150 is configured to enable
execution of queries over grouped data. Given a query, the query
processor component 150, utilizing known or novel mechanisms, can
extract and return results that satisfy the query from the data
store 140. A visualization, or diagnostic tool, for example, can
employ the query processor component 150 to acquire data.
[0035] Visualization component 160 is configured to enable grouped
profile data to be visualized. The profile data can be rendered in
an arbitrary number of ways rather than being limited to
conventional call trees. Further, since profile data is grouped in
a semantically meaningful level, visualizations can exploit
groupings and further aid a human user in understanding profile
data. In accordance with one embodiment, the visualization
component 160 can spawn a graphical user interface that graphically
depicts grouped profile data and enables interaction with the data.
For example, a query can be authored over the grouped data by the
visualization component 160 or a human user and filtered results
returned as a function of the query. Further, a mechanism can be
provided to allow users to drill down to acquire more detailed data
as well as rollup to view data at a more abstract/high-level.
[0036] While the program profile system 100 includes a
visualization component 160, it should be appreciated that
visualization or diagnostic tools can be external to the program
profile system. In this scenario, interaction can be accomplished
in a similar manner, for example by querying the profile system for
requisite data. Furthermore, note that the visualization component
160 can be extendable in various ways. For example, initially the
visualization component 160 can support a first set of
visualizations. Subsequently, a second set of visualizations can be
added by way of a third-party plugin or through an update/upgrade
to the program profile system 100, for instance.
[0037] Subscription component 170 provides an additional or
alternate manner of disseminating grouped data. More specifically,
grouped data housed in data store 140 can be delivered in
accordance with a publish/subscribe model. The subscription
component 170 can offer and manage subscriptions and publish the
grouped data to interested subscribers.
[0038] The program profile system 100 is not limited to operating
with respect to real-time execution of a program, or, stated
differently, as profile data is collected. Additionally, the
program profile system 100 can operate with respect to historical
data, or at a time post-collection. For example, an arbitrary
organizational scheme can be overlaid on a persisted log to enable
data to be viewed at a meaningful level in terms of program
function and structure. In other words, an arbitrary number of
views can be raised against trace data by applying alternate
groupings.
[0039] As shown in FIG. 1, the organizational scheme 114 can be
external to not only the program profile system 100 but also the
program 112. This has many benefits including, among others,
portability and allowing the organizational scheme to be changed
easily. However, the disclosed subject matter is not limited
thereto. In an alternate embodiment, the organizational scheme 114
can be embedded with the program 112, or a version thereof, as
shown in FIG. 2. For example, the organizational scheme can be
encoded in such a manner that the information is provided with
profile data such as part of an instrumentation event. More
specifically, the organizational scheme can be encoded in program
function names. Additionally or alternatively, a string identifier
indicative of a group or sub-group can be associated with a
function or other portion of a program and raised with an
event.
[0040] Still further yet, rather than encoding the organizational
scheme 114 in the program 112, a process for generating the
organizational scheme 114 can be encoded in the program 112. The
scheme generation component 130 can then automatically generate the
organizational scheme 114 based on the encoded process, for
instance at runtime. As an example, at runtime, an arbitrary
grouping can be created by binary name, such that all profile data
collected during execution of a first binary is ascribed to the
first binary and all profile data collected when a second binary is
executed is ascribed to the second binary. Additionally, the scheme
generation component 130 can be configured to automatically update
the organizational scheme 114 in real-time, for example as data is
being collected or as part of dynamically regenerating a new view
on a collected log.
[0041] FIG. 3 depicts a representative data-collection component
110. As previously described, the data collection component 110 is
configured to acquire profiling data from a program that can
subsequently be grouped as a function of an organizational scheme.
The data collection component 110 includes a sample component 310
and an instrument component 320. The sample component 310 is
configured to initiate sampling of a program during execution, for
example by making appropriate calls to an operating system and
acquiring the results. In one instance, a sample is a stack that is
collected at a specific moment in time (either as a result of a
specific code operation, such as an allocation event, or as part of
an organized collection processes from which a general picture of
code execution will be created). The instrument component 320 is
configured to receive, retrieve or otherwise obtain or acquire data
resulting from program instrumentation. Here, data can be probe
data, effectively a start/stop pair of events, for example. In one
instance, start/stop events are employed to acquire a payload. In
other instances, the start stop events can be utilized to demarcate
an interesting boundary (no explicit payload). A time stamp and
call stack can be acquired for any generated event. Accordingly, an
event can be said to implicitly carry those payloads as well. A
sample can be thought of as the system forcing a "code is
executing" event with a time stamp and current call stack payload.
Further, note that events can be marked with an activity identifier
that can flow across various contexts, such as threads, into other
events. This data can be used, therefore, to correlate profile data
in a flexible manner.
[0042] Combination component 330 is configured to enable use of
both instrumentation and sampling in various manners. For instance,
data collected by each approach can augment the other approach.
More specifically, data can be combined, utilized to cross check
results, and/or statistically normalized, among other things. In
this manner, advantages of both approached can be exploited and
disadvantages can be mitigated. By way of example and not
limitation, inherently less precise sample data can be supplemented
with more precise instrumentation data.
[0043] Context detection component 340 is configured to detect a
context switch or in other words, a change in processing context.
For example, a context switch can occur with regard to multiple
processes sharing a CPU. In such a multitasking environment, a
process can be halted and the state of a CPU stored such that the
process can be resumed from the same point at a later time. Stated
differently, one process is switched out of a central processing
unit so that another process can run. In one instance, a context
switch can be considered a special type of event. Once a context
switch is detected with respect to a program being profiled, data
collection can be suspended until processing resumes or collected
data can be marked such that the program profile system 100 can
differentiate data associated with a program from data that is not.
In this manner, data can be excluded. For example, duration of
function calls, of time-to-execute, can exclude time spent
executing processes unrelated to a program being profiled, or in
other words periods of inactivity with respect to a program being
profiled due to a context switch.
[0044] Consider the following organizational scheme authored in XML
in conjunction with FIG. 4, in which tags identify groups.
TABLE-US-00001 <?xml version="1.0" encoding="utf-8"?>
<TagSet Name="ExampleTagSet"> <Tag Name="A">
<Events> <EventStart Provider="ExampleProvider"
Symbol="A_Activity1_START"/> <EventStop
Provider="ExampleProvider" Symbol="A_Activity1_STOP"/>
</Events> <Tag Name="B"> <Events> <EventStart
Provider="ExampleProvider" Symbol="B_Activity1_START"/>
<EventStop Provider="ExampleProvider"
Symbol="B_Activity1_STOP"/> </Events> <Tag Name="C">
<Events> <EventStart Provider="ExampleProvider"
Symbol="C_Activity1_START"/> <EventStop
Provider="ExampleProvider" Symbol="C_Activity1_STOP"/>
</Events> </Tag> </Tag> </Tag>
</TagSet>
This organizational scheme concerns an instrumented approach to
data collection including a plurality of start/stop event pairs for
three arbitrary groups (a.k.a. tags) "A," "B," and "C." FIG. 6
shows an event stream timeline 410 of nine milliseconds, which is a
level of granularity chosen solely for illustrative purposes. In
this timeline 410, an event associated with group/tag "A" starts,
as indicated in the organizational scheme as "A_Activity1_START."
During execution and prior to the an exit event associated with tag
"A," an event associated with group/tag "B" fires, noted as
"B_Activity1_START." An event pertaining to group/tag "C"
("C_Activity1_START) occurs at millisecond three while "A" and "B"
are in progress, and an exit event ("C_Activity1_STOP") occurs
before millisecond four. At millisecond four, a context switch
occurs and this particular line of activity is suspended. As
indicated, no execution data will be attributed during milliseconds
four, five, and six. At millisecond seven, tag "B" finishes
execution, and at millisecond eight, tag "A" finishes. At
millisecond nine, a start/stop pair for tag "C" occurs. Because
neither tag "A" nor tag "B" is in progress, this time unit is added
to tag "C." Accordingly, there is time 420 before the context
switch, time 430 during the context switch and time 440 after the
context switch. During time 410, one millisecond is attributed to
each of group/tag "A," "B," and "C." During time 430, no time is
attributed to any group. During time 430, one millisecond is
attributed to each of group/tag "A," "B," and "C."
[0045] The following is a similar scenario except that sampling is
the data collection mechanism rather than instrumentation. Consider
the following organizational scheme specified in XML in conjunction
with FIG. 5, in which tags identify groups and the structure
defines hierarchical relationships between groups.
TABLE-US-00002 <?xml version="1.0" encoding="utf-8"?>
<TagSet Name="ExampleTagSet"> <Tag Name="A">
<Entrypoint Module="BinaryOne.dll" Method="ClassOne::A"/>
<Tag Name="B"> <Entrypoint Module="BinaryOne.dll"
Method="ClassOne::B"/> <Tag Name="C"> <Entrypoint
Module="BinaryTwo.dll" Method="HelperClass::C"/> </Tag>
</Tag> </Tag> </TagSet>
Here, the organizational scheme is defined for groupings of stack
samples. As shown in FIG. 5, six stacks are collected, three during
time 510 before a context switch, zero during time 520
corresponding to the context switch, and three during time 530
after the context switch. In the first sample 512, solely function
"A" is executing. In the second sample 514, function "B" has been
called by function "A" resulting of a stack of "A" and "B" (A::B).
As defined by the organizational scheme, time associated with this
code stack is attributable to "B." In the third sample 516,
function "C" has been called by function "B" which was called by
function "A" resulting in a stack of "A," "B," and "C." In
accordance with the organizational scheme, time for this code stack
is attributed to "C." There are no samples collected during time
520 due to the context switch. In the fourth sample 532, solely
functions "A" and "B" remain in execution resulting in a stack of
"A" and "B" (A::B), and time is attributed to "B." In the fifth
sample 534, the code stack includes solely function "A"
consequently resulting in time being attributed to "A." Next, in
sample six 536, function "C" is called by function "D" resulting in
a stack of "D" and "C" (D::C). In this case, "C" is the closest
frame associated with a defined group and it is therefore added to
group/tag "C." Note that neither "A" nor "B" is required to be on
the stack in order to provide this bucketing as might be assumed
strictly on the basis of examining the hierarchical XML.
[0046] FIG. 5 is also helpful for clarifying the role of priority
in the system. Imagine that group "C" has been given a lower
priority than "A" or "B" (countermanding an implied priority
established by the fact that children are, in the absence of any
other explicit mark, higher in priority than parents). In this
scenario, the third collected stack "A::B::C" would actually be
attributed to group "B," as the highest priority group in the
analyzed stack. The sixth collected stack "D::C" would still be
ascribed to group "C," as described above.
[0047] Note also that an organizational scheme, such as the
previous organizational scheme, can employ wild cards and/or
pattern matching with respect to symbol/event names. Consider, for
example, use of a star wildcard character as in "module=`*`
method=`malloc`". Here, an allocation routine will be called across
all modules. As another example consider "method=`ClassOne::*`". In
this case, all members of "ClassOne" are identified.
[0048] Returning to FIG. 3, additional details are provided about
how instrumentation and sampling can be employed with respect to
grouping. The instrumentation approach utilizes a number of event
start/stop pairs to demarcate a meaningful boundary and separate
profile data. In this scenario, an event is a signal that an
executing program has hit a specific point in code. Here, there is
a pair of events associated with start and stop. For example, a
start event can indicate that page rendering has begun and a stop
event can indicate that the page rendering has terminated. Further,
suppose there is an arbitrary group "A" that is to include data
associated with event "B" and event "C." When event "B" and event
"C" start and stop, intervening execution time can be aggregated
and ascribed to group "A." The intervals specified with start stop
pairs can thus be coalesced into a logical group "A." In other
words, a number of events can be utilized to demarcate boundaries
for groupings. Further, note that events can be associated with
non-time-to-execute profile data including bytes or object
allocated, page refreshes, registry reads/writes, and threads
spawned, among other things. Accordingly, an event can indicate
that "X" bytes were allocated, for example.
[0049] As per sampling approach, rather than having event
start/stop pairs, a sampled stack and timestamp are employed.
Groups can be defined with respect to one or more particular
stacks. In other words, a subset of stacks is mapped to one or more
logical groups. In the instrumentation approach, it is known when
an event such as "C" fires. However, there is also a stack
corresponding event "C" firing. Additionally, event "C" may be
fired from multiple places, so there can many different stacks that
can be attributed to group "A." For example, it can be indicated
that stack "A::B::C" and stack "X::Y::C" are indicative of event
"C" and maps to group "A" but stack "D::E::C" does not. In other
words, empiric code conditions are detected that indicate that
event "C" might have fired and as such associated profile data
should be ascribed to logical group "A." With instrumentation, it
is possible to determine precisely what is happening in a program,
whereas sampling is an approximation. Sampling essentially
indicates that a certain percentage of the time when the program
was checked profiling data was ascribed to logical group A.
[0050] When working with event start/stop pairs, these pairs lead
to logical groupings and allow ascription of associated profile
data and aggregation of intervening data points without consulting
a stack. For example, when an event such as rendering starts, bytes
are allocated, and subsequently the rendering event stops, the
bytes allocated are ascribed to group "A," for example. There is no
need to consult a stack. However, if event start/stop pairs are not
employed, there can be a stack associated with bytes allocated. In
this case, it can be determined or inferred that the bytes
allocated are attributable to group "A." Therefore, stacks in
combination with additional data allow a system to work back to the
same event start/stop pair grouping. In another scenario, where
solely sample stacks are employed time to execute sampling can be
employed. Here, the information able to be revealed is the
approximate time spent executing in a logical group or the like. In
other words, the samples themselves are logically grouped.
[0051] It is to be appreciated that grouping information need not
be limited to that which is expressed in an organizational scheme
to organize profile data. By way of example, consider a data stream
that interleaves grouping start/stop event pairs with another event
of interest such as bytes allocated. In this case, the grouping
start/stop event pairs can be utilized as a basis for grouping the
bytes allocated event. In other words, bytes allocated can be
ascribed to a group associated with the event start/stop pairs. Of
course, stack-grouping information expressed in an organizational
scheme can be utilized and applied to a callback associated with an
event payload, for instance.
[0052] FIGS. 6 and 7 are exemplary screenshots of visualization of
profile data associated with a web browser. Further, the
visualization can be produced as a function of an organizational
scheme such as the one provided in APPENDIX A. Here, the
organizational scheme is encoded as an XML document in which tags
identify groups and the structure defines hierarchical
relationships between groups. FIGS. 6 and 7 are provided to
facilitate clarity and understanding with respect to aspects of
this disclosure and not to limit the scope of the appended claims
thereto. One of skill in the art will recognize upon reading this
description that there are various other manners of visualizing
grouped profile data all of which are within the spirit and scope
of this disclosure.
[0053] FIG. 6 is an exemplary screenshot of a table visualization
of profile data. More specifically, the screenshot depicts a
detailed central processor unit (CPU) utilization breakdown by
subsystem as defined by an organizational scheme. The table
specifies groups and subgroups with respect to a plurality of rows
and columns. Column 610, labeled "Process," specifies the root of a
hierarchy of groups corresponding to the single binary or
executable. Column 615 provides a grouping by thread identifiers.
For each thread identifier there are three levels of sub-groups as
denoted by columns 620, 625, and 630. For instance, column 620
identifies a browser-rendering engine, named "Trident," which is
broken down into "Layout," and "Display," among other things noted
in column 625. Further, "Layout," in column 625, is further broken
down into "BuildBlocks" and "BuildLayout" groups, among others
noted in column 630. Profile data is noted and aggregated for each
group and sub-group and is provided in columns 635, 640, 645, 650,
660, and 665 corresponding respectively to start time, end time,
duration, percentage of CPU utilization, percentage of time and
count. From this table, high-level meaningful information is
communicated. From a quick glance, a user can determine that the
browser is spending too much time in layout and thus optimization
efforts can focus on portions of the program dealing with the
layout.
[0054] FIG. 7 is an exemplary screenshot showing CPU utilization
breakdown of execution by subsystem, as defined by the
organizational scheme specified in Appendix A. Two views, 710 and
750, are shown to enable comparison of two collection mechanisms
instrumentation (e.g., start/stop pairs) and sampling (e.g., stack
samples. Each of view 710 and view 750 include respective bar
graphs 730 and 770 plotting percentage usage of a single central
processing unit over time. Further, each graph distinguishes
groups, which selectable by way of checkboxes in windows 720 and
760.
[0055] The aforementioned systems, architectures, environments, and
the like have been described with respect to interaction between
several components. It should be appreciated that such systems and
components can include those components or sub-components specified
therein, some of the specified components or sub-components, and/or
additional components. Sub-components could also be implemented as
components communicatively coupled to other components rather than
included within parent components. Further yet, one or more
components and/or sub-components may be combined into a single
component to provide aggregate functionality. Communication between
systems, components and/or sub-components can be accomplished in
accordance with either a push and/or pull model. The components may
also interact with one or more other components not specifically
described herein for the sake of brevity, but known by those of
skill in the art.
[0056] Furthermore, various portions of the disclosed systems above
and methods below can include or employ of artificial intelligence,
machine learning, or knowledge or rule-based components,
sub-components, processes, means, methodologies, or mechanisms
(e.g., support vector machines, neural networks, expert systems,
Bayesian belief networks, fuzzy logic, data fusion engines,
classifiers . . . ). Such components, inter alia, can automate
certain mechanisms or processes performed thereby to make portions
of the systems and methods more adaptive as well as efficient and
intelligent. By way of example, and not limitation, the scheme
generation component 130 can utilize such mechanism to infer groups
based on available information.
[0057] In view of the exemplary systems described supra,
methodologies that may be implemented in accordance with the
disclosed subject matter will be better appreciated with reference
to the flow charts of FIGS. 8 and 9. While for purposes of
simplicity of explanation, the methodologies are shown and
described as a series of blocks, it is to be understood and
appreciated that the claimed subject matter is not limited by the
order of the blocks, as some blocks may occur in different orders
and/or concurrently with other blocks from what is depicted and
described herein. Moreover, not all illustrated blocks may be
required to implement the methods described hereinafter.
[0058] Referring to FIG. 8, a method of program profiling 800 is
illustrated. At reference numeral 810, profile data can be
acquired. In one instance, such data can be acquired by way code
instrumentation, sampling, or both. Alternatively, historical
profile data can be received, retrieved, or otherwise obtained or
acquired from a log or other persisted file, for example. At
numeral 820, profile data can be ascribed to groups as a function
of an organizational scheme that defines groups and relationships
between groups. In other words, the organizational scheme can be
overlaid on the profile data. Further, the groupings can be
associated with high-level functions of a program or a sub-system
of the program such that a group is an abstraction that is
meaningful for program analysis. As an example, execution time and
central processor utilization associated with a first set of
functions responsible for layout of content in a browser window can
be ascribed to group "A" while the same data associated with a
second set of functions that displays the browser window can be
ascribed to group "B." Further and in accordance with one
embodiment, profile data associated with a context switch is
excluded. At reference numeral 830, data can be rendered based on
the ascribed groups to visualize grouped data and aid diagnosing
performance issues. In other words, the profile data can be
rendered in terms of groups ascribed thereto. In one instance,
grouped data can be housed in a data store that can be queried to
retrieve requisite data for a visualization. Further, the
visualization can be embodied as a graphical user interface that
can enable human users to issue or modify queries as well as
influence the manner in which data resulting from such queries is
presented. For example, such queries can enable zooming in and out
with respect to a timeline. Further, alternate organizational
schemes may be applied to the data to further aid analysis.
[0059] FIG. 9 is a flow chart diagram depicting a method of
grouping profile data. At reference numeral 910, an organizational
scheme is received, retrieved, or otherwise obtained or acquired.
The organizational scheme can specify groups and relationships
between groups. In one instance, the organizational scheme can be
manually authored optionally utilizing pattern matching or other
filtering mechanisms (e.g., regular expressions, use of wild-card
characters . . . ) to aid specification. Additionally or
alternatively, the organizational scheme can be generated
automatically or semi-automatically based on context or other
available information. At numeral 920, data structures can be
created, or instantiated based on the scheme. For instance,
structures can be created for each defined group. A reference
numeral 930, the created data structures can be populated with
profile data, for instance as it is received. In this manner,
profiling data can be ascribed to a group. Subsequently, the
grouped data can be queried or saved for later use.
[0060] The word "exemplary" or various forms thereof are used
herein to mean serving as an example, instance, or illustration.
Any aspect or design described herein as "exemplary" is not
necessarily to be construed as preferred or advantageous over other
aspects or designs. Furthermore, examples are provided solely for
purposes of clarity and understanding and are not meant to limit or
restrict the claimed subject matter or relevant portions of this
disclosure in any manner. It is to be appreciated a myriad of
additional or alternate examples of varying scope could have been
presented, but have been omitted for purposes of brevity.
[0061] As used herein, the terms "component," and "system," as well
as various forms thereof (e.g., components, systems, sub-systems .
. . ) are intended to refer to a computer-related entity, either
hardware, a combination of hardware and software, software, or
software in execution. For example, a component may be, but is not
limited to being, a process running on a processor, a processor, an
object, an instance, an executable, a thread of execution, a
program, and/or a computer. By way of illustration, both an
application running on a computer and the computer can be a
component. One or more components may reside within a process
and/or thread of execution and a component may be localized on one
computer and/or distributed between two or more computers.
[0062] The conjunction "or" as used this description and appended
claims in is intended to mean an inclusive "or" rather than an
exclusive "or," unless otherwise specified or clear from context.
In other words, "`X` or `Y`" is intended to mean any inclusive
permutations of "X" and "Y." For example, if `"A` employs `X,`" "`A
employs `Y,`" or "`A` employs both `X` and `Y,`" then "`A` employs
`X` or `Y`" is satisfied under any of the foregoing instances.
[0063] As used herein, the term "inference" or "infer" refers
generally to the process of reasoning about or inferring states of
the system, environment, and/or user from a set of observations as
captured via events and/or data. Inference can be employed to
identify a specific context or action, or can generate a
probability distribution over states, for example. The inference
can be probabilistic--that is, the computation of a probability
distribution over states of interest based on a consideration of
data and events. Inference can also refer to techniques employed
for composing higher-level events from a set of events and/or data.
Such inference results in the construction of new events or actions
from a set of observed events and/or stored event data, whether or
not the events are correlated in close temporal proximity, and
whether the events and data come from one or several event and data
sources. Various classification schemes and/or systems (e.g.,
support vector machines, neural networks, expert systems, Bayesian
belief networks, fuzzy logic, data fusion engines . . . ) can be
employed in connection with performing automatic and/or inferred
action in connection with the claimed subject matter.
[0064] Furthermore, to the extent that the terms "includes,"
"contains," "has," "having" or variations in form thereof are used
in either the detailed description or the claims, such terms are
intended to be inclusive in a manner similar to the term
"comprising" as "comprising" is interpreted when employed as a
transitional word in a claim.
[0065] In order to provide a context for the claimed subject
matter, FIG. 10 as well as the following discussion are intended to
provide a brief, general description of a suitable environment in
which various aspects of the subject matter can be implemented. The
suitable environment, however, is only an example and is not
intended to suggest any limitation as to scope of use or
functionality.
[0066] While the above disclosed system and methods can be
described in the general context of computer-executable
instructions of a program that runs on one or more computers, those
skilled in the art will recognize that aspects can also be
implemented in combination with other program modules or the like.
Generally, program modules include routines, programs, components,
data structures, among other things that perform particular tasks
and/or implement particular abstract data types. Moreover, those
skilled in the art will appreciate that the above systems and
methods can be practiced with various computer system
configurations, including single-processor, multi-processor or
multi-core processor computer systems, mini-computing devices,
mainframe computers, as well as personal computers, hand-held
computing devices (e.g., personal digital assistant (PDA), phone,
watch . . . ), microprocessor-based or programmable consumer or
industrial electronics, and the like. Aspects can also be practiced
in distributed computing environments where tasks are performed by
remote processing devices that are linked through a communications
network. However, some, if not all aspects of the claimed subject
matter can be practiced on stand-alone computers. In a distributed
computing environment, program modules may be located in one or
both of local and remote memory storage devices.
[0067] With reference to FIG. 10, illustrated is an example
general-purpose computer 1010 or computing device (e.g., desktop,
laptop, server, hand-held, programmable consumer or industrial
electronics, set-top box, game system . . . ). The computer 1010
includes one or more processor(s) 1020, memory 1030, system bus
940, mass storage 1050, and one or more interface components 1070.
The system bus 1040 communicatively couples at least the above
system components. However, it is to be appreciated that in its
simplest form the computer 1010 can include one or more processors
1020 coupled to memory 1030 that execute various computer
executable actions, instructions, and or components stored in
memory 1030.
[0068] The processor(s) 1020 can be implemented with a general
purpose processor, a digital signal processor (DSP), an application
specific integrated circuit (ASIC), a field programmable gate array
(FPGA) or other programmable logic device, discrete gate or
transistor logic, discrete hardware components, or any combination
thereof designed to perform the functions described herein. A
general-purpose processor may be a microprocessor, but in the
alternative, the processor may be any processor, controller,
microcontroller, or state machine. The processor(s) 1020 may also
be implemented as a combination of computing devices, for example a
combination of a DSP and a microprocessor, a plurality of
microprocessors, multi-core processors, one or more microprocessors
in conjunction with a DSP core, or any other such
configuration.
[0069] The computer 1010 can include or otherwise interact with a
variety of computer-readable media to facilitate control of the
computer 1010 to implement one or more aspects of the claimed
subject matter. The computer-readable media can be any available
media that can be accessed by the computer 1010 and includes
volatile and nonvolatile media, and removable and non-removable
media. By way of example, and not limitation, computer-readable
media may comprise computer storage media and communication
media.
[0070] Computer storage media includes volatile and nonvolatile,
removable and non-removable media implemented in any method or
technology for storage of information such as computer-readable
instructions, data structures, program modules, or other data.
Computer storage media includes, but is not limited to memory
devices (e.g., random access memory (RAM), read-only memory (ROM),
electrically erasable programmable read-only memory (EEPROM) . . .
), magnetic storage devices (e.g., hard disk, floppy disk,
cassettes, tape . . . ), optical disks (e.g., compact disk (CD),
digital versatile disk (DVD) . . . ), and solid state devices
(e.g., solid state drive (SSD), flash memory drive (e.g., card,
stick, key drive . . . ) . . . ), or any other medium which can be
used to store the desired information and which can be accessed by
the computer 1010.
[0071] Communication media typically embodies computer-readable
instructions, data structures, program modules, or other data in a
modulated data signal such as a carrier wave or other transport
mechanism and includes any information delivery media. The term
"modulated data signal" means a signal that has one or more of its
characteristics set or changed in such a manner as to encode
information in the signal. By way of example, and not limitation,
communication media includes wired media such as a wired network or
direct-wired connection, and wireless media such as acoustic, RF,
infrared and other wireless media. Combinations of any of the above
should also be included within the scope of computer-readable
media.
[0072] Memory 1030 and mass storage 1050 are examples of
computer-readable storage media. Depending on the exact
configuration and type of computing device, memory 1030 may be
volatile (e.g., RAM), non-volatile (e.g., ROM, flash memory . . . )
or some combination of the two. By way of example, the basic
input/output system (BIOS), including basic routines to transfer
information between elements within the computer 1010, such as
during start-up, can be stored in nonvolatile memory, while
volatile memory can act as external cache memory to facilitate
processing by the processor(s) 1020, among other things.
[0073] Mass storage 1050 includes removable/non-removable,
volatile/non-volatile computer storage media for storage of large
amounts of data relative to the memory 1030. For example, mass
storage 1050 includes, but is not limited to, one or more devices
such as a magnetic or optical disk drive, floppy disk drive, flash
memory, solid-state drive, or memory stick.
[0074] Memory 1030 and mass storage 1050 can include, or have
stored therein, operating system 1060, one or more applications
1062, one or more program modules 1064, and data 1066. The
operating system 1060 acts to control and allocate resources of the
computer 1010. Applications 1062 include one or both of system and
application software and can exploit management of resources by the
operating system 1060 through program modules 1064 and data 1066
stored in memory 1030 and/or mass storage 1050 to perform one or
more actions. Accordingly, applications 1062 can turn a
general-purpose computer 1010 into a specialized machine in
accordance with the logic provided thereby.
[0075] All or portions of the claimed subject matter can be
implemented using standard programming and/or engineering
techniques to produce software, firmware, hardware, or any
combination thereof to control a computer to realize the disclosed
functionality. By way of example and not limitation, the program
profile system 100, or portions thereof, can be, or form part, of
an application 1062, and include one or more modules 1064 and data
1066 stored in memory and/or mass storage 1050 whose functionality
can be realized when executed by one or more processor(s) 1020.
[0076] In accordance with one particular embodiment, the
processor(s) 1020 can correspond to a system on a chip (SOC) or
like architecture including, or in other words integrating, both
hardware and software on a single integrated circuit substrate.
Here, the processor(s) 1020 can include one or more processors as
well as memory at least similar to processor(s) 1020 and memory
1030, among other things. Conventional processors include a minimal
amount of hardware and software and rely extensively on external
hardware and software. By contrast, an SOC implementation of
processor is more powerful, as it embeds hardware and software
therein that enable particular functionality with minimal or no
reliance on external hardware and software. For example, the
program profile system 100 and/or associated functionality can be
embedded within hardware in a SOC architecture.
[0077] The computer 1010 also includes one or more interface
components 1070 that are communicatively coupled to the system bus
1040 and facilitate interaction with the computer 1010. By way of
example, the interface component 1070 can be a port (e.g., serial,
parallel, PCMCIA, USB, FireWire . . . ) or an interface card (e.g.,
sound, video . . . ) or the like. In one example implementation,
the interface component 1070 can be embodied as a user input/output
interface to enable a user to enter commands and information into
the computer 1010 through one or more input devices (e.g., pointing
device such as a mouse, trackball, stylus, touch pad, keyboard,
microphone, joystick, game pad, satellite dish, scanner, camera,
other computer . . . ). For instance, an input device can capture
user gestures or voice commands. In another example implementation,
the interface component 1070 can be embodied as an output
peripheral interface to supply output to displays (e.g., CRT, LCD,
plasma . . . ), speakers, printers, and/or other computers, among
other things. Still further yet, the interface component 1070 can
be embodied as a network interface to enable communication with
other computing devices (not shown), such as over a wired or
wireless communications link.
[0078] What has been described above includes examples of aspects
of the claimed subject matter. It is, of course, not possible to
describe every conceivable combination of components or
methodologies for purposes of describing the claimed subject
matter, but one of ordinary skill in the art may recognize that
many further combinations and permutations of the disclosed subject
matter are possible. Accordingly, the disclosed subject matter is
intended to embrace all such alterations, modifications, and
variations that fall within the spirit and scope of the appended
claims.
* * * * *