Re^2: Perl in data science: could a grant from Perl foundation be useful?

That's a fair point and I have doubts if financial support would have a desired effect too. However, I think that in most cases the scientist is not forced to use one particular programming language as Python, R, SAS, MATLAB, Julia, Perl or something else. The problem is not that some particular tool is necessary or not. The problem is that PDL and other Perl modules are not known well enough as options and are not attractive enough for people that could use them. For example I only recently discovered that such thing as PDL exists and did not know this option before.

To summarize, I think that the need for usage of a particular programming language in data science is much more limited than its actual usage. The goal might be to make Perl a more attractive option not only for those who need it and cannot avoid it but also for those who do not need any particular option and are considering which of these to choose in order to achieve their goals.

Comment on Re^2: Perl in data science: could a grant from Perl foundation be useful?

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Re^3: Perl in data science: could a grant from Perl foundation be useful? by hda (Chaplain) on Mar 14, 2020 at 11:14 UTC
Agree that researchers are not forced to use one particular language. The choice of programming language and tools is almost free when working alone. However, when working in groups that share code it is more difficult to freely choose. Nowadays, it can be almost impossible be the only one using Perl while all others code in Python or R, unless the particular workflow and local culture allows it. For example if only exchanging data chunks in some standard format, instead of code. I believe that the major barrier to widespread use of Perl is that it has largely been displaced by Python and R in the general culture of scientific circles. Technical details or idiosyncrasies are weak arguments often used in discussions, but I believe the real thing is just cultural.	[reply]
Re^4: Perl in data science: could a grant from Perl foundation be useful? by etj (Priest) on Jul 27, 2024 at 17:22 UTC
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.	[reply]
Re^5: Perl in data science: could a grant from Perl foundation be useful? by marioroy (Prior) on Aug 01, 2024 at 10:58 UTC
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 盜 37286.043 盜 36175.966 盜 32786.131 盜 34714.937 盜 32924.175 盜 33201.933 盜 33097.029 盜 33265.114 盜 33904.076 盜 $ python numba_prange.py ================================================== In place in (Python Numba - CPU multi-threaded) ================================================== 30696.392 盜 37623.167 盜 32857.656 盜 26406.765 盜 26831.150 盜 26624.441 盜 26728.153 盜 26698.828 盜 26748.657 盜 26932.001 盜 $ 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 盜 45389.175 盜 37540.913 盜 29339.075 盜 28975.725 盜 29037.952 盜 30524.731 盜 29897.928 盜 30224.562 盜 28925.419 盜 [download] GPU results captured on NVIDIA GeForce RTX 3070. $ python cupy_blas.py ================================================== In place in (Python CuPy - GPU cuBLAS) ================================================== 6246.805 盜 24.557 盜 17.881 盜 17.643 盜 17.643 盜 17.643 盜 17.643 盜 17.405 盜 17.405 盜 17.405 盜 $ python numba_cuda.py ================================================== In place in (Python Numba - GPU CUDA) ================================================== 182.629 盜 40.054 盜 30.279 盜 28.133 盜 27.895 盜 27.657 盜 27.418 盜 26.941 盜 26.941 盜 26.703 盜 $ 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 盜 51.975 盜 28.133 盜 25.034 盜 24.080 盜 26.464 盜 26.226 盜 24.557 盜 24.080 盜 28.133 盜 [download]	[reply] [d/l] [select]
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
Re^4: Perl in data science: could a grant from Perl foundation be useful? by Anonymous Monk on Mar 14, 2020 at 15:06 UTC
I believe that the major barrier to widespread use of Perl is that it has largely been displaced There is only one book left on the shelf...	[reply]