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One of the reasons I keep dropping hints about #perlffi , #pdl and #openmp is that one can literally have five multithreading frameworks in the same #perl application of a master process: 1) PDL, and FFI intefacing with 2) #Fortran coarrays, 3) Fortran openmp, 4) #c openmp and 5) #cplusplus #openmp. All these frameworks can share memory addresses for array and vector objects, and #perl aided by #PerlAlien makes the authoring of the high-level code a pleasure ! https://hgpu.org/?p=29403

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The final installment in the series:

"The-Quest-For-Performance" from my blog Killing It with #perl

Discussing #python #numpy #numba, #rstats #openMP enhancements of Perl code and #simd

Bottom line: I will not be migrating to Python anytime soon.

Food for thought: The Perl interpreter (and many of the modules) are deep down massive C programs. Perhaps one can squeeze real performance kicks by looking into alternative compilers, compiler flags and pragmas ?

https://chrisarg.github.io/Killing-It-with-PERL/2024/07/09/The-Quest-For-Performance-Part-IV-May-the-SIMD-Force-Be-With-You.html

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A couple of data/compute intensive examples using Perl Data Language (#PDL), #OpenMP, #Perl, Inline and #Python (base, #numpy, #numba). Kind of interesting to see Python eat Perl's dust and PDL being equal to numpy.

OpenMP and Perl's multithreaded #PDL array language were the clear winners here.


https://chrisarg.github.io/Killing-It-with-PERL/2024/07/06/The-Quest-For-Performance-Part-I-InlineC-OpenMP-PDL.html

https://chrisarg.github.io/Killing-It-with-PERL/2024/07/07/The-Quest-For-Performance-Part-II-PerlVsPython.md.html

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Enhancing non-Perl bioinformatic applications with #Perl: Building novel, component based applications using Object Orientation, PDL, Alien, FFI, Inline and OpenMP - Archive ouverte HAL https://hal.science/hal-04606172v1

Preprint for the #TPRC2024 talk to be delivered in 10days

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