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

[download]

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

[download]

In reply to Re^5: Perl in data science: could a grant from Perl foundation be useful? by marioroy
in thread Perl in data science: could a grant from Perl foundation be useful? by zubenel0

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