array
bqn-libs
array | bqn-libs | |
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5 | 1 | |
189 | 45 | |
- | - | |
6.9 | 6.9 | |
5 months ago | 4 months ago | |
C++ | ||
Apache License 2.0 | BSD Zero Clause License |
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array
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Einsum in 40 Lines of Python
I wrote a library in C++ (I know, probably a non-starter for most reading this) that I think does most of what you want, as well as some other requests in this thread (generalized to more than just multiply-add): https://github.com/dsharlet/array?tab=readme-ov-file#einstei....
A matrix multiply written with this looks like this:
enum { i = 2, j = 0, k = 1 };
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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...
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A basic introduction to NumPy's einsum
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.
bqn-libs
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Ngn/k (free K implementation)
Languages with multidimensional arrays (APL, BQN, J, but not K) have trouble with dicts because an index into an array is a list of numbers, and an index into a dict is an arbitrary value. Many primitives, and especially selection, are designed around lists of numbers and don't transfer to dicts. In K, where the index into a list is one number, there's still a requirement that the keys in a dict all have the same level of nesting, but this isn't bad in practice. BQN will eventually have hashmaps implemented as in a more mainstream/conventional way, as objects. There's a model at https://github.com/mlochbaum/bqn-libs/blob/master/hashmap.bq... .
I don't think studying the compiler is a very good way to learn BQN, but I would like to write up parts of it (limited by time and motivation of course). I did some chat sessions on this sort of compilation during early development; see the links at the bottom of https://mlochbaum.github.io/BQN/implementation/ .
What are some alternatives?
optimizing-the-memory-layout-of-std-tuple - Optimizing the memory layout of std::tuple
Kbd - Alternative unified APL keyboard layouts (AltGr, Backtick, Compositions)
NumPy - The fundamental package for scientific computing with Python.
PDP_11_Simulator - PDP11 Simulator written in APL
cadabra2 - A field-theory motivated approach to computer algebra.
kona - Open-source implementation of the K programming language
alphafold2 - To eventually become an unofficial Pytorch implementation / replication of Alphafold2, as details of the architecture get released
array - Simple array language written in kotlin
Einsum.jl - Einstein summation notation in Julia
kerf1 - Kerf (Kerf1) is a columnar tick database and time-series language for Linux/OSX/BSD/iOS/Android. It is written in C and natively speaks JSON and SQL. Kerf can be used for trading platforms, feedhandlers, low-latency networking, high-volume analysis of realtime and historical data, logfile processing, and more.
c-examples - Example C code
aoc2017 - ngn/k