January 05, 2015 topic: tagged: • • • • In this post I give an overview of my evaluation of three different CPU profiling tools: gperftools, Valgrind and gprof. Torrent trilogy total bass module. I evaluated the three tools on usage, functionality, accuracy and runtime overhead. The usage of the different profilers is demonstrated with the small demo program, available via my github repository. The intent of cpuload.cpp is just to generate some CPU load - it does nothing useful. The bash scripts in the same repo (which are also listed below) show how to compile/link the cpuload.cpp appropriately and execute the resulting executable to get the CPU profiling data.

Screenshot tutorial to show you how to install Android on bq Ubuntu Phone using Ubuntu. GPROF Tutorial – How to use. In this article, we will explore the GNU profiling tool. Ubuntu Tutorials; PostgreSQL DB. This weekend saw unofficial WhatsApp support arrive on the Ubuntu Phone, with the arrival of. How to Install Open Store on Ubuntu Phone.

Gprof The GNU profiler uses a hybrid approach of compiler assisted instrumentation and sampling. Instrumentation is used to gather function call information (e.g. To be able to generate call graphs and count the number of function calls). To gather profiling information at runtime, a sampling process is used. This means, that the program counter is probed at regular intervals by interrupting the program with operating system interrupts. As sampling is a statistical process, the resulting profiling data is not exact but are rather a statistical approximation. Creating a CPU profile of your application with gprof requires the following steps: • compile and link the program with a compatible compiler and profiling enabled (e.g.

• execute your program to generate the profiling data file (default filename: gmon.out) • run gprof to analyze the profiling data Let’s apply this to our demo application. #!/bin/bash # build the program with profiling support (-gp) g++ -std =c++11 -pg cpuload.cpp -o cpuload # run the program; generates the profiling data file (gmon.out)./cpuload # print the callgraph gprof cpuload The gprof output consists of two parts: the flat profile and the call graph. The flat profile reports the total execution time spent in each function and its percentage of the total running time. Function call counts are also reported. Output is sorted by percentage, with hot spots at the top of the list. Geni cwz-12j-dmx manual pdf. Gprof’s call graph is a textual call graph representation which shows the caller and callees of each function.

For detailed information on how to interpret the callgraph, take a look at the. You can also generate a graphical representation of the callgraph with - a tool to generate a graphical representation of the gprof callgraph)). The overhead (mainly caused by instrumentation) can be quite high: estimated to 30-260%. Gprof does not support profiling multi-threaded applications and also cannot profile shared libraries. Even if there exist workarounds to get threading support, the fact that it cannot profile calls into shared libraries, makes it totally unsuitable for today’s real-world projects. Valgrind/callgrind is an instrumentation framework for building dynamic analysis tools.

Valgrind is basically a virtual machine with just in time recompilation of x86 machine code to some simpler RISC-like intermediate code: UCode. It does not execute x86 machine code directly but it “simulates” the on the fly generated UCode. There are various Valgrind based for debugging and profiling purposes. Depending on the chosen tool, the UCode is instrumented appropriately to record the data of interest. For performance profiling, we are interested in the tool callgrind: a profiling tool that records the function call history as a call-graph. For analyzing the collected profiling data, there is is the amazing visualization tool.

It represents the collected data in a very nice way what tremendously helps to get an overview about whats going on. Creating a CPU profile of your application with valgrind/callgrind is really simple and requires the following steps: • compile your program with debugging symbols enabled (to get a meaningful call-graph) • execute your program with valgrind --tool=callgrind./yourprogram to generate the profiling data file • analyze your profiling data with e.g. KCachegrind Let’s apply this our demo application (profile_valgrind.sh).