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{{Short description|Measuring the time or resources used by a section of a computer program}}
{{more citations needed|date=January 2009}}
{{Software development process|Tools}}
In [[software engineering]], '''profiling''' (
Profiling is achieved by [[Instrumentation (computer programming)|instrumenting]] either the program [[source code]] or its binary executable form using a tool called a ''profiler'' (or ''code profiler''). Profilers may use a number of different techniques, such as event-based, statistical, instrumented, and simulation methods.
== Gathering program events ==
Profilers use a wide variety of techniques to collect data, including [[hardware interrupt]]s, [[Instrumentation (computer programming)|code instrumentation]], [[instruction set simulator|instruction set simulation]], operating system [[hooking|hooks]], and [[Hardware performance counter|performance counter]]s.
== Use of profilers ==
[[File:CodeAnalyst3.png|thumb|Graphical output of the [[CodeAnalyst]] profiler
{{quotation|text=
Program analysis tools are extremely important for understanding program behavior. Computer architects need such tools to evaluate how well programs will perform on new [[computer architecture|architectures]]. Software writers need tools to analyze their programs and identify critical sections of code. [[Compiler]] writers often use such tools to find out how well their [[instruction scheduling]] or [[branch prediction]] algorithm is performing...|author=ATOM|source=[[Conference on Programming Language Design and Implementation|PLDI]]|'94}}
The output of a profiler may be:
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/* ------------ source------------------------- count */
0001
0002 THEN DO
0003 ADD 1 to XCOUNT
0004 ELSE
0005
* A stream of recorded events (a '''trace''')
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===Input-sensitive profiler===
Input-sensitive profilers<ref name="aprof">E. Coppa, C. Demetrescu, and I. Finocchi, [https://web.archive.org/web/20180611201601/https://ieeexplore.ieee.org/document/6858059/ ''Input-Sensitive Profiling''], IEEE Trans. Software Eng. 40(12): 1185-1205 (2014); [[doi:10.1109/TSE.2014.2339825]]</ref><ref>D. Zaparanuks and M. Hauswirth, ''Algorithmic Profiling'', Proceedings of the 33rd ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI 2012), ACM SIGPLAN Notices, Vol. 47, No. 6, pp. 67-76, 2012; [[doi:10.1145/2254064.2254074]]</ref><ref>T. Kustner, J. Weidendorfer, and T. Weinzierl, ''Argument Controlled Profiling'', Proceedings of Euro-Par 2009 – Parallel Processing Workshops, Lecture Notes in Computer Science, Vol. 6043, pp. 177-184, 2010; [[doi:10.1007/978-3-642-14122-5 22]]</ref> add a further dimension to flat or call-graph profilers by relating performance measures to features of the input workloads, such as input size or input values. They generate charts that characterize how an application's performance scales as a function of its input.
==Data granularity in profiler types==
Profilers, which are also programs themselves, analyze target programs by collecting information on
===Event-based profilers===
Event-based profilers are available for the following programming languages:
* [[Java (programming language)|Java]]: the [[Java Virtual Machine Tools Interface|JVMTI]] (JVM Tools Interface) API, formerly JVMPI (JVM Profiling Interface), provides hooks to profilers, for trapping events like calls, class-load, unload, thread enter leave.
* [[.NET Framework|.NET]]: Can attach a profiling agent as a ''COM'' server to the ''CLR'' using Profiling ''API''. Like Java, the runtime then provides various callbacks into the agent, for trapping events like method [[Interpreter|JIT]] / enter / leave, object creation, etc. Particularly powerful in that the profiling agent can rewrite the target application's bytecode in arbitrary ways.
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===Statistical profilers===
In practice, sampling profilers can often provide a more accurate picture of the target program's execution than other approaches, as they are not as intrusive to the target program
Unfortunately, running kernel code to handle the interrupts incurs a minor loss of CPU cycles from the target program, diverts cache usage, and cannot distinguish the various tasks occurring in uninterruptible kernel code (microsecond-range activity) from user code. Dedicated hardware can
▲In practice, sampling profilers can often provide a more accurate picture of the target program's execution than other approaches, as they are not as intrusive to the target program, and thus don't have as many side effects (such as on memory caches or instruction decoding pipelines). Also since they don't affect the execution speed as much, they can detect issues that would otherwise be hidden. They are also relatively immune to over-evaluating the cost of small, frequently called routines or 'tight' loops. They can show the relative amount of time spent in user mode versus interruptible kernel mode such as [[system call]] processing.
Some commonly used<ref>{{cite web| title=Popular C# Profilers| publisher=Gingtage| year=2014| url=http://www.ginktage.com/2014/10/popular-c-profilers/}}</ref> statistical profilers for Java/managed code are [[SmartBear Software]]'s [[AQtime]]<ref>{{cite web| work=AQTime 8 Reference| title=Sampling Profiler - Overview| publisher=SmartBear Software| year=2018| url=https://support.smartbear.com/viewarticle/54581/}}</ref> and [[Microsoft]]'s [[CLR Profiler]].<ref>{{cite web| work=Microsoft .NET Framework Unmanaged API Reference| last=Wenzal| first=Maira|display-authors=etal| title=Profiling Overview| publisher=Microsoft| year=2017| url=https://docs.microsoft.com/en-us/dotnet/framework/unmanaged-api/profiling/profiling-overview#supported-features}}</ref> Those profilers also support native code profiling, along with [[Apple Inc.]]'s [[Apple Developer Tools#Shark|Shark]] (OSX),<ref>{{cite web| work=[[Apple Developer Tools]]| title=Performance Tools| publisher=Apple, Inc.| year=2013| url=https://developer.apple.com/library/content/documentation/Performance/Conceptual/PerformanceOverview/PerformanceTools/PerformanceTools.html}}</ref> [[OProfile]] (Linux),<ref>{{cite web| work=
▲Dedicated hardware can go beyond this: ARM Cortex-M3 and some recent MIPS processors JTAG interface have a PCSAMPLE register, which samples the [[program counter]] in a truly undetectable manner, allowing non-intrusive collection of a flat profile.
▲Some commonly used<ref>{{cite web| title=Popular C# Profilers| publisher=Gingtage| year=2014| url=http://www.ginktage.com/2014/10/popular-c-profilers/}}</ref> statistical profilers for Java/managed code are [[SmartBear Software]]'s [[AQtime]]<ref>{{cite web| work=AQTime 8 Reference| title=Sampling Profiler - Overview| publisher=SmartBear Software| year=2018| url=https://support.smartbear.com/viewarticle/54581/}}</ref> and [[Microsoft]]'s [[CLR Profiler]].<ref>{{cite web| work=Microsoft .NET Framework Unmanaged API Reference| last=Wenzal| first=Maira|display-authors=etal| title=Profiling Overview| publisher=Microsoft| year=2017| url=https://docs.microsoft.com/en-us/dotnet/framework/unmanaged-api/profiling/profiling-overview#supported-features}}</ref> Those profilers also support native code profiling, along with [[Apple Inc.]]'s [[Apple Developer Tools#Shark|Shark]] (OSX),<ref>{{cite web| work=[[Apple Developer Tools]]| title=Performance Tools| publisher=Apple, Inc.| year=2013| url=https://developer.apple.com/library/content/documentation/Performance/Conceptual/PerformanceOverview/PerformanceTools/PerformanceTools.html}}</ref> [[OProfile]] (Linux),<ref>{{cite web| work=[[IBM DeveloperWorks]]| last1=Netto| first1=Zanella| last2=Arnold| first2=Ryan S.| title=Evaluate performance for Linux on Power| year=2012| url=https://www.ibm.com/developerworks/linux/library/l-evaluatelinuxonpower/}}</ref> [[Intel]] [[VTune]] and Parallel Amplifier (part of [[Intel Parallel Studio]]), and [[Oracle Corporation|Oracle]] [[Performance Analyzer]],<ref>{{cite conference |last1=Schmidl |first1=Dirk |first2=Christian |last2=Terboven |first3=Dieter |last3=an Mey |first4=Matthias S. |last4=Müller |title=Suitability of Performance Tools for OpenMP Task-Parallel Programs |conference=Proc. 7th Int'l Workshop on Parallel Tools for High Performance Computing |year=2013 |pages=25–37 |isbn=9783319081441 |url=https://books.google.com/books?id=-I64BAAAQBAJ&pg=PA27}}</ref> among others.
===Instrumentation ===
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* '''Interpreter debug''' options can enable the collection of performance metrics as the interpreter encounters each target statement. A [[bytecode]], [[control table]] or [[Just-in-time compilation|JIT]] interpreters are three examples that usually have complete control over execution of the target code, thus enabling extremely comprehensive data collection opportunities.
===Hypervisor/
* '''Hypervisor''': Data are collected by running the (usually) unmodified program under a [[hypervisor]]. Example: [[SIMMON]]
* '''Simulator''' and '''Hypervisor''': Data collected interactively and selectively by running the unmodified program under an [[
==See also==
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