Catch VS benchmark

Identification: Use tools like pytest-rerunfailures, TestNg, and Catch2 to automatically rerun failed tests.

This is one area where a BDD style framework like catch2[0] really shines, IMO. The way tests are written in this style naturally lends itself to self-documenting each branch

[0]: https://github.com/catchorg/Catch2

The code uses catch2 to test for correctness and performance. Catch2 is a unit testing framework for C++, but it also provides basic micro-benchmarking features. Unfortunately, it doesn't provide any way to visualise the results, so I created a script and submitted PR adding plotting capabilities to Catch.

https://github.com/catchorg/Catch2 (for unit testing, need to do timing yourself)

```c++ // https://github.com/catchorg/Catch2/tree/Catch1.x // https://github.com/philsquared/Catch/releases/download/v1.12.2/catch.hpp

Include(FetchContent) FetchContent_Declare(Catch2 GIT_REPOSITORY https://github.com/catchorg/Catch2.git GIT_TAG v3.3.2 ) FetchContent_MakeAvailable(Catch2)

As with the assembly code analysis, let's look at two types of operations: sequentially filling an array with random numbers, and randomly accessing arbitrary array elements when we pass it to a function. To compile the code, we used GCC and Clang. We checked both debug (with the -DDEBUG flag) and release builds (with the -O3 level optimizations and the -DNDEBUG flag). And of course, we used Google Benchmark to run the tests.

''' Uchen core - ML framework ''' module(name = "uchen-core", version = "0.1", compatibility_level = 1) bazel_dep(name = "abseil-cpp", version = "20240116.2") bazel_dep(name = "googletest", version = "1.14.0") git_override( module_name = "googletest", remote = "https://github.com/google/googletest.git", commit = "1d17ea141d2c11b8917d2c7d029f1c4e2b9769b2", ) bazel_dep(name = "google_benchmark", version = "1.8.3") git_override( module_name = "google_benchmark", remote = "https://github.com/google/benchmark.git", commit = "447752540c71f34d5d71046e08192db181e9b02b", ) # Dev dependencies bazel_dep(name = "hedron_compile_commands", dev_dependency = True) git_override( module_name = "hedron_compile_commands", remote = "https://github.com/hedronvision/bazel-compile-commands-extractor.git", commit = "a14ad3a64e7bf398ab48105aaa0348e032ac87f8", )

I'm aware of one by Google that I used a couple times, but IMO it's better to capture real runtime data from a fully-operational process than to carve out the benchmarkable bits and test them in isolation, so I track information during program testing and print it all to a log instead of using things like that.

If you just want to do some quick benchmarks, you can just use std::chrono::high_resolution_clock::now(). Call it before the code that you are benchmarking and then immediately after. Take them away and you have your duration. If you want to use a proper benchmarking tool then I can totally recommend Google Benchmark. Fantastic benchmarking tool. Honourable mention would be Quick Bench which is an online tool that uses Google Benchmark.

I tried to install google benchmark(https://github.com/google/benchmark) in my ubuntu machine by :

We don't know if it's a lie until we verify it and that's not difficult, you have a quicksort implementation in a couple of languages, you'll need to pass the necessary parameters to show the time needed by a function call to execute to the compiler or interpreter or you may use use a library(like benchmark for C++) and you're good to go.

If you are uncertain about the performance characteristics of a function you should ALWAYS benchmark it. Googles Benchmark library is wonderful for quick micro benchmarks. For more complex things, perhaps look into profiling and then look at invocation counts of copy constructors.