in reply to Re^4: Perl in data science: could a grant from Perl foundation be useful?
in thread Perl in data science: could a grant from Perl foundation be useful?

I think https://chrisarg.github.io/Killing-It-with-PERL/2024/07/07/The-Quest-For-Performance-Part-II-PerlVsPython.md.html et al make a powerful argument that Perl (especially with PDL) can be a powerful, and high-performance, adjunct to other programming languages or tools in data science, and other science.

I created complementary demonstrations for the article on the web, running on the CPU or GPU.

CPU results captured on AMD Threadripper 3970X.

$ perl pdl_cpu.pl ================================================== In place in (Perl PDL - CPU multi-threaded) ================================================== 33974.886 µs 37286.043 µs 36175.966 µs 32786.131 µs 34714.937 µs 32924.175 µs 33201.933 µs 33097.029 µs 33265.114 µs 33904.076 µs $ python numba_prange.py ================================================== In place in (Python Numba - CPU multi-threaded) ================================================== 30696.392 µs 37623.167 µs 32857.656 µs 26406.765 µs 26831.150 µs 26624.441 µs 26728.153 µs 26698.828 µs 26748.657 µs 26932.001 µs $ python taichi_cpu.py [Taichi] version 1.8.0, llvm 15.0.4, commit 37a05638, linux, python 3. +10.13 ================================================== In place in (Python Taichi - CPU multi-threaded) ================================================== [Taichi] Starting on arch=x64 76603.651 µs 45389.175 µs 37540.913 µs 29339.075 µs 28975.725 µs 29037.952 µs 30524.731 µs 29897.928 µs 30224.562 µs 28925.419 µs

GPU results captured on NVIDIA GeForce RTX 3070.

$ python cupy_blas.py ================================================== In place in (Python CuPy - GPU cuBLAS) ================================================== 6246.805 µs 24.557 µs 17.881 µs 17.643 µs 17.643 µs 17.643 µs 17.643 µs 17.405 µs 17.405 µs 17.405 µs $ python numba_cuda.py ================================================== In place in (Python Numba - GPU CUDA) ================================================== 182.629 µs 40.054 µs 30.279 µs 28.133 µs 27.895 µs 27.657 µs 27.418 µs 26.941 µs 26.941 µs 26.703 µs $ python taichi_cuda.py [Taichi] version 1.8.0, llvm 15.0.4, commit 37a05638, linux, python 3. +10.13 ================================================== In place in (Python Taichi - GPU CUDA) ================================================== [Taichi] Starting on arch=cuda 15263.557 µs 51.975 µs 28.133 µs 25.034 µs 24.080 µs 26.464 µs 26.226 µs 24.557 µs 24.080 µs 28.133 µs

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Re^6: Perl in data science: could a grant from Perl foundation be useful?
by marioroy (Prior) on Aug 04, 2024 at 04:54 UTC