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APL.jl | array | |
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3 | 4 | |
62 | 187 | |
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0.0 | 6.9 | |
almost 2 years ago | 3 months ago | |
Julia | C++ | |
GNU General Public License v3.0 or later | Apache License 2.0 |
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APL.jl
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The counter-intuitive rise of Python in scientific computing (2020)
2. ipython repl
1. pairs with jaimebuelta's artistic vs engineering dichotomy, but also plays into the scientist wearing many more hats than just programmer. Code can be two or more degrees removed from the published paper -- code isn't the passion. There isn't reason, time, or motivation to think deeply about syntax.
2. For a lot of academic work, the programming language is primarily an interface to an advanced plotting calculator. Or at least that's how I think about the popularity of SPSS and Stata. Ipython and then jupyter made this easy for python.
For what it's worth, the lab I work for is mostly using shell, R, matlab, and tiny bit of python. For numerical analysis, I like R the best. It has a leg up on the interactive interface and feels more flexible than the other two. R also has better stats libraries. But when we need to interact with external services or file formats, python is the place to look (why PyPI beat out CPAN is similar question).
Total aside: Perl's built in regexp syntax is amazing and a thing I reach for often, but regular expressions as a DSL are supported almost everywhere (like using languages other than shell to launch programs and pipes -- totally find but misses all the ergonomics of using the right tool for the job). It'd love to explore APL as an analogous numerical DSL across scripting languages. APL.jl [0] and, less practically april[1], are exciting.
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Symbolic Programming
APL.jl might be of interest to you.
- Try APL
array
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Benchmarking 20 programming languages on N-queens and matrix multiplication
I should have mentioned somewhere, I disabled threading for OpenBLAS, so it is comparing one thread to one thread. Parallelism would be easy to add, but I tend to want the thread parallelism outside code like this anyways.
As for the inner loop not being well optimized... the disassembly looks like the same basic thing as OpenBLAS. There's disassembly in the comments of that file to show what code it generates, I'd love to know what you think is lacking! The only difference between the one I linked and this is prefetching and outer loop ordering: https://github.com/dsharlet/array/blob/master/examples/linea...
This gets to 90% of BLAS: https://github.com/dsharlet/array/blob/38f8ce332fc4e26af0832...
But this is quite general. I’m claiming you can beat BLAS if you have some unique knowledge of the problem that you can exploit. For example, some kinds of sparsity can be implemented within the above example code yet still far outperform the more general sparsity supported by MKL and similar.
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A basic introduction to NumPy's einsum
Compilers can be pretty good if you help them out a bit. Here's my implementation of Einstein reductions (including summations) in C++, which generate pretty close to ideal code until you start getting into processor architecture specific optimizations: https://github.com/dsharlet/array#einstein-reductions
If you are looking for something like this in C++, here's my attempt at implementing it: https://github.com/dsharlet/array#einstein-reductions
It doesn't do any automatic optimization of the loops like some of the projects linked in this thread, but, it provides all the tools needed for humans to express the code in a way that a good compiler can turn it into really good code.
What are some alternatives?
ngn-apl - An APL interpreter written in JavaScript. Runs in a browser or NodeJS.
ride - Remote IDE for Dyalog APL
array - Simple array language written in kotlin
json - A tiny JSON parser and emitter for Perl 6 on Rakudo
julia - The Julia Programming Language
optimizing-the-memory-layout-of-std-tuple - Optimizing the memory layout of std::tuple
nlvm - LLVM-based compiler for the Nim language
conan - Conan - The open-source C and C++ package manager
Arraymancer - A fast, ergonomic and portable tensor library in Nim with a deep learning focus for CPU, GPU and embedded devices via OpenMP, Cuda and OpenCL backends
Nim - Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula. Its design focuses on efficiency, expressiveness, and elegance (in that order of priority).
NumPy - The fundamental package for scientific computing with Python.
aplette - This is a new take on an old language: APL. The goal is to pare APL down to its elegant essence. This version of APL is oriented toward scripting within a Unix-style computing environment.