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Lightweight cross-language pseudo-profiling for C, C++, and Python reporting timing [wall, user, system, cpu, %cpu] and resident set size (RSS) memory [current page allocation and peak usage]

Project description

TiMemory’s design is aimed at routine (“everyday”) timing and memory analysis that can be standard part of the source code.

TiMemory is a very lightweight, cross-language timing and memory utility. It support implementation in C, C++, and Python and is easily imported into CMake projects.

TiMemory is Lightweight and Fast

Analysis on a fibonacci calculation determined that each TiMemory “auto-timer” adds an average overhead of 9 microseconds (0.000009 s) without memory measurements and 16 microseconds (0.000016 s) with memory measurements. This performance is specific to the machine and the overhead for a particular machine can be calculated by running the test_cxx_overhead example.

Since TiMemory only records information of the functions explicitly specified, you can safely assume that unless TiMemory is inserted into a function called > 100,000 times, it won’t be adding more than a second of runtime to the function. Therefore, there is a simple rule of thumb: don’t insert a TiMemory auto-timer into very simple functions that get called very frequently.

TiMemory is not intended to replace profiling tools such as Intel’s VTune, GProf, etc. – instead, it complements them by enabling one to verify timing and memory usage without the overhead of the profiler.

TiMemory is Cross-Language: C, C++, and Python

It is very common for Python projects to implement expensive routines in C or C++. Implementing a TiMemory auto-timer in any combination of these languages will produce one combined report for all the languages (provided each language links to the same library). However, this is a feature of TiMemory. TiMemory can be used in standalone C, C++, or Python projects.

TiMemory is thread-safe with minimal locking

All TiMemory auto-timers reference a timer storaged in a thread-local singleton “manager” class. It is never recommended to directly create a “manager” instance. Instead, call the static function tim::manager::instance() or if the master thread instance is desired: tim::manager::master_instance(). It is generally safe to delete the master instance at the end of the application. The master instance is a raw pointer in C++. The only locking that occurs within TiMemory is on the destruction of a non-master-thread instance of the manager – which is automatically done via the use of a shared_ptr in C++ when the thread exits.

TiMemory supports MPI

If a project uses MPI, TiMemory will combined the reports from all the MPI ranks when a report is requested.

TiMemory has built-in timing and memory plotting

The results from TiMemory can be serialized to JSON and the JSON output can be used to produce timing and memory performance plots via the standalone timemory-plotter or timemory.plotting Python module

TiMemory reports temporary memory usage

Memory reports permit determination of temporary memory usage until the “high-water mark” of memory allocation is reached

TiMemory can be used from the command-line

UNIX systems provide timem executable that works like time. On all systems, pytimem is provided.

$ timem bash -c "for i in {1..5}; do sleep 1; done"

> [bash] total execution time  : 5.052 wall, 0.010 user + 0.010 system = 0.020 cpu (  0.4%) [sec], 2.2 peak rss [MB]

$ pytimem bash -c "for i in {1..5}; do sleep 1; done"

> [bash] total execution time  : 5.043 wall, 0.010 user + 0.020 system = 0.030 cpu (  0.6%) [sec], 2.1 peak rss [MB]

pytimem has the benefit of being able to define a timemory_json_handler module in the CWD that handles the serialization data (such as submitting the data to a server). Here is an example

#!/usr/bin/env python

def receive(args, json_obj):
    print('\n{} received json object for "{}"\n'.format(__file__, args))

    print('json dictionary\n{}'.format(json_obj))

TiMemory has environment controls


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