Profiling (computer programming) - Wikipedia. In software engineering, profiling (. Most commonly, profiling information serves to aid program optimization. Profiling is achieved by 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 are used in the performance engineering process. Use of profilers. Computer architects need such tools to evaluate how well programs will perform on new 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.. The output of a profiler may be: A statistical summary of the events observed (a profile)Summary profile information is often shown annotated against the source code statements where the events occur, so the size of measurement data is linear to the code size of the program. A trace may therefore be initiated at one point in a program and terminated at another point to limit the output. An ongoing interaction with the hypervisor (continuous or periodic monitoring via on- screen display for instance)This provides the opportunity to switch a trace on or off at any desired point during execution in addition to viewing on- going metrics about the (still executing) program. It also provides the opportunity to suspend asynchronous processes at critical points to examine interactions with other parallel processes in more detail. History. In early 1. In 1. 98. 2 gprof extended the concept to a complete call graph analysis. The ATOM platform converts a program into its own profiler: at compile time, it inserts code into the program to be analyzed. That inserted code outputs analysis data. This technique - modifying a program to analyze itself - is known as . In some tools full context is not preserved. Input- sensitive profiler. They generate charts that characterize how an application's performance scales as a function of its input. Data granularity in profiler types. The Chromium Projects. SyzyProf is an open source instrumenting hierarchical performance profiler that works well with Chrome. The program allows comprehensive profiling of Chrome code, including profiling over tasks or IPC, as. Based on their data granularity, on how profilers collect information, they are classified into event based or statistical profilers. Since profilers interrupt program execution to collect information, they have a finite resolution in the time measurements, which should be taken with a grain of salt. Event- based profilers. Like Java, the runtime then provides various callbacks into the agent, for trapping events like method JIT / enter / leave, object creation, etc. Particularly powerful in that the profiling agent can rewrite the target application's bytecode in arbitrary ways. Python: Python profiling includes the profile module, hotshot (which is call- graph based), and using the 'sys. Flat- profiler in profile. C- extension are present. Statistical profilers. A sampling profiler probes the target program's call stack at regular intervals using operating systeminterrupts. Sampling profiles are typically less numerically accurate and specific, but allow the target program to run at near full speed. The resulting data are not exact, but a statistical approximation. In fact, if a value is n times the sampling period, the expected error in it is the square- root of n sampling periods. 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. Still, kernel code to handle the interrupts entails a minor loss of CPU cycles, diverted cache usage, and is unable to distinguish the various tasks occurring in uninterruptible kernel code (microsecond- range activity). Sampling CPU Profiling in Chrome Built-in sampling CPU profiler in. Profiles tab in Developer Tools! Structural Profiler (chrome://tracing) Clearly showed program execution flow and timings Required additional. Chrome, Opera and Internet Explorer. The Opera community has created a program. Home > Tutorials > How To Create New Web Browser User Profiles. Make Google chrome with specific user profile as default browser. C:\Program Files (x86)\Google\Chrome\Application\chrome.exe. Profiling memory performance. This tutorial explains how to use the Heap Profiler for uncovering memory leaks in your. Basic functions Taking a. 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 of the most commonly used statistical profilers are AMDCode. Analyst, Apple Inc. Shark (OSX), oprofile (Linux). Note that instrumenting a program can cause performance changes, and may in some cases lead to inaccurate results and/or heisenbugs. The effect will depend on what information is being collected, and on the level of detail required. For example, adding code to count every procedure/routine call will probably have less effect than counting how many times each statement is obeyed. A few computers have special hardware to collecting information; in this case the impact on the program is minimal. Instrumentation is key to determining the level of control and amount of time resolution available to the profilers. The following lists a few popular tools (as of 2. Example: Parasoft. Insure++Intermediate language: instrumentation added to assembly or decompiled bytecodes giving support for multiple higher- level source languages and avoiding (non- symbolic) binary offset re- writing issues, for example Open. PAT. Compiler assisted: Gprof, Quantify, Micro. Profiler and Intel(R) Single Event API use this approach. The program run is fully supervised and controlled by the tool. Examples: Pin, Valgrind, Dynamo. RIO. Runtime injection: More lightweight than runtime instrumentation. Code is modified at runtime to have jumps to helper functions. Example: Dyn. Inst. Interpreter instrumentation. A bytecode, control table or JIT interpreters are three examples that usually have complete control over execution of the target code, thus enabling extremely comprehensive data collection opportunities. Hypervisor/Simulator. Example: SIMMONSimulator and Hypervisor: Data collected interactively and selectively by running the unmodified program under an Instruction Set Simulator. Publishing Chrome in a Citrix virtualized environment. Learn the steps involved when publishing Chrome as a virtualized application for Citrix XenApp.See also. Mc. Kusick, gprof: a Call Graph Execution Profiler, Proceedings of the SIGPLAN '8. Symposium on Compiler Construction, SIGPLAN Notices, Vol. A. Eustace, ATOM: A system for building customized program analysis tools, Proceedings of the ACM SIGPLAN Conference on Programming language design and implementation (PLDI '9. ACM SIGPLAN Notices - Best of PLDI 1. Homepage archive, Vol. Years of PLDI (1. Finocchi, Input- Sensitive Profiling, Proceedings of the 3. ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI 2. ACM SIGPLAN Notices, Vol. D. Hauswirth, Algorithmic Profiling, Proceedings of the 3. ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI 2. ACM SIGPLAN Notices, Vol. T. Weinzierl, Argument Controlled Profiling, Proceedings of Euro- Par 2. Statistical Inaccuracy of gprof Output^Schmidl, Dirk; Terboven, Christian; an Mey, Dieter; M. Suitability of Performance Tools for Open. MP Task- Parallel Programs. Int'l Workshop on Parallel Tools for High Performance Computing.
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