On Thursday, 13 January 2022 at 15:09:13 UTC, Ola Fosheim Grøstad wrote:
On Thursday, 13 January 2022 at 14:50:59 UTC, bachmeier wrote:
If you write code in Python, it's realistically only for the Python world. Probably the same for Julia.
Does scipy provide the functionality you would need? Could it in some sense be considered a baseline for scientific computing APIs?
SciPy is fairly useful but it is only one amongst a constellation of Python scientific computing libraries. It emulates a fair amount of what is provided by MATLAB, and it sits on top of numpy. Using SciPy, numpy, and matplotlib in tandem gives a user access to roughly the same functionality as a vanilla installation of MATLAB.
SciPy and numpy are built on top of a substrate of old and stable packages written in Fortran and C (BLAS, LAPACK, fftw, etc.).
Python, MATLAB, and Julia are basically targeted at scientists and engineers writing "application code". These languages aren't appropriate for "low-level" scientific computing along the lines of the libraries mentioned above. Julia does make a claim to the contrary: it is feasible to write fast low-level kernels in it, but (last time I checked) it is not so straightforward to export them to other languages, since Julia likes to do things at runtime.
Fortran and C remain good choices for low-level kernel development because they are easily consumed by Python et al. And as far as parallelism goes, OpenMP is the most common since it is straightforward conceptually. C++ is also fairly popular but since consuming something like a highly templatized header-only C++ library using e.g. Python's FFI is a pain, it is a less natural choice. (It's easier using pybind11, but the compile times will make you weep.)
Fortran, C, and C++ are also all standardized. This is valuable. The people developing these libraries are---more often than not---academics, who aren't able to devote much of their time to software development. Having some confidence that their programming language isn't going to change underneath gives them some assurance that they aren't going to be forced to spend an inordinate amount of time keeping their code in compliance for it to remain usable. Either that, or they write a library in Python and abandon it later.
As an aside, people lament the use of MATLAB, but one of its stated goals is backwards compatibility. Consequently, there's rather a lot of old MATLAB code floating around still in use.
"High-level" D is currently not that interesting for high-level scientific application code. There is a long list of "everyday" scientific computing tasks I could think of which I'd like to be able to execute in a small number of lines, but this is currently impossible using any flavor of D. See https://www.numerical-tours.com for some ideas.
"BetterC" D could be useful for developing numerical kernels. An interesting idea would to use D's introspection capabilities to automatically generate wrappers and documentation for each commonly used scientific programming language (Python, MATLAB, Julia). But D not being standardized makes it less attractive than C or Fortran. It is also unclear how stable D is as an open source project. The community surrounding it is rather small and doesn't seem to have much momentum. There also do not appear to be any scientific computing success stories with D.
My personal view is that people in science are generally more interested in actually doing science than in playing around with programming trivia. Having to spend time to understand something like C++'s argument dependent lookup is generally viewed as undesirable and a waste of time.