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I am in a similar situation, as I develop a lot of scientific code. I've tested several solutions, and so far the most viable for me (though far from perfect) i

I am in a similar situation, as I develop a lot of scientific code. I've tested several solutions, and so far the most viable for me (though far from perfect) is Python+NumPy+Fortran, the latter exposed to Python using f2py (much easier than binding C/C++ to Python). Not sure if this might be a good solution for you, it depends whether the bottleneck in your code is in I/O or in raw calculations (in the former case you're not going to get any significant advantage from Fortran). I've tested several other languages that might be good for my purposes. From what I have seen, Rust is not ideal because of the acknowledged poor support for multi dimensional arrays [1] and the strange semantics for floats (due to the need to be «correct» in presence of NaNs). Two options that might become interesting once they gain a stable status are Julia [2] and Chapel [3]. The former might be exactly what you are you looking for; the latter is ok if you usually run your codes on HPC platforms. But be prepared to face some friction when sharing your work with colleagues or publishing papers: the little penetration of these two languages in the scientific world implies that people will face difficulties in using/understanding how your code works. [1] Look for «array» in the page https://users.rust-lang.org/t/on-rust-goals-in-2018-and-beyond/12336 https://users.rust-lang.org/t/on-rust-goals-in-2018-and-beyo... [2] https://julialang.org/ https://julialang.org/ [3] http://chapel.cray.com/ http://chapel.cray.com/

zombinedev 9y ago
D has quite good multi-dimensional array support, though probably not as flexible as Python. One of the best libraries in this area is ndslice (part of mir-algorithm) - http://docs.algorithm.dlang.io/latest/index.html http://docs.algorithm.dlang.io/latest/index.html. See the "Image Processing" example to get a sense of API - http://docs.algorithm.dlang.io/latest/mir_ndslice.html http://docs.algorithm.dlang.io/latest/mir_ndslice.html. mir-algorithm is the base for the high-performance glas implementation written in pure D that outperforms libraries with hand-written assembly like OpenBLAS in single-threaded mode (multi-threaded is not yet ready). See http://blog.mir.dlang.io/glas/benchmark/openblas/2016/09/23/glas-gemm-benchmark.html http://blog.mir.dlang.io/glas/benchmark/openblas/2016/09/23/.... (Note that this blog post is bit outdated - since then the author rewrote large parts of the library, IIRC.)
nnq 9y ago
Julia could have had 10x the adoption if they painted themselves as more of a general purpose language, like "write the web app and your neural networks (GPU accelerable bien sur) in the same language ftw!"... if only they'd have bolted in some support for "non-weird-looking classic OOP" like in "wanna type `window.` and have even the most retarded editor autocomplete methods". Imho they got it backwards: Python got so popular in scientific computing because it was first and foremost a general-purpose scripting language and didn't isolate (library-wise) sci-coders from all the other developers, second because it was designed as a "teaching language" so simple examples looked close enough to pseudocode so were liked by people in the business of having to explain their code (academia), and only lastly because it happen to be easily extensible for things like matrix operation (operator overloading...) and had non-weird general syntax (like in "functions are value and that's that" not Ruby-like weird blocks and procs that spooked math and physics folks...). My only hope for "general purpose & sci computing" language would be Go if they dragged they heads out their asses and added some damn sugar to the langue - like operator overloading, some parametric types or templates (good luck bothering a physicists to understand when to use type switches and type assertions...), and something to allow "casual" developers to not be bitten by things like "not all nill's are equal, wtf?!" things. I like Go, but it's a hard language to sell to a non-100%-professional-developer...
- Volt 9y ago
  They're going after Matlab, Octave and R rather than Python.
  - nnq 9y ago
    That's why I think "they get things backwards" :| ...Python went after Matlab, Octave and R. And for ML/AI is almost completely replaced them. For stats and data-exploration it shares the pie with R because they are so different languages with different strengths. It's a shame that the best language designers seem to also be the worst at "market positioning" and programming languages a pretty much fashion & promotion driven area. Anyway, maybe I should shut up, find the time to delve into Julia and when I know enough try to give them a helping hand instead of whining...

zombinedev 9y ago

D has quite good multi-dimensional array support, though probably not as flexible as Python. One of the best libraries in this area is ndslice (part of mir-algorithm) - http://docs.algorithm.dlang.io/latest/index.html http://docs.algorithm.dlang.io/latest/index.html. See the "Image Processing" example to get a sense of API - http://docs.algorithm.dlang.io/latest/mir_ndslice.html http://docs.algorithm.dlang.io/latest/mir_ndslice.html. mir-algorithm is the base for the high-performance glas implementation written in pure D that outperforms libraries with hand-written assembly like OpenBLAS in single-threaded mode (multi-threaded is not yet ready). See http://blog.mir.dlang.io/glas/benchmark/openblas/2016/09/23/glas-gemm-benchmark.html http://blog.mir.dlang.io/glas/benchmark/openblas/2016/09/23/.... (Note that this blog post is bit outdated - since then the author rewrote large parts of the library, IIRC.)

nnq 9y ago

Julia could have had 10x the adoption if they painted themselves as more of a general purpose language, like "write the web app and your neural networks (GPU accelerable bien sur) in the same language ftw!"... if only they'd have bolted in some support for "non-weird-looking classic OOP" like in "wanna type `window.` and have even the most retarded editor autocomplete methods". Imho they got it backwards: Python got so popular in scientific computing because it was first and foremost a general-purpose scripting language and didn't isolate (library-wise) sci-coders from all the other developers, second because it was designed as a "teaching language" so simple examples looked close enough to pseudocode so were liked by people in the business of having to explain their code (academia), and only lastly because it happen to be easily extensible for things like matrix operation (operator overloading...) and had non-weird general syntax (like in "functions are value and that's that" not Ruby-like weird blocks and procs that spooked math and physics folks...). My only hope for "general purpose & sci computing" language would be Go if they dragged they heads out their asses and added some damn sugar to the langue - like operator overloading, some parametric types or templates (good luck bothering a physicists to understand when to use type switches and type assertions...), and something to allow "casual" developers to not be bitten by things like "not all nill's are equal, wtf?!" things. I like Go, but it's a hard language to sell to a non-100%-professional-developer...

Volt 9y ago
They're going after Matlab, Octave and R rather than Python.
- nnq 9y ago
  That's why I think "they get things backwards" :| ...Python went after Matlab, Octave and R. And for ML/AI is almost completely replaced them. For stats and data-exploration it shares the pie with R because they are so different languages with different strengths. It's a shame that the best language designers seem to also be the worst at "market positioning" and programming languages a pretty much fashion & promotion driven area. Anyway, maybe I should shut up, find the time to delve into Julia and when I know enough try to give them a helping hand instead of whining...