Tullio.jl | einop | |
---|---|---|
4 | 2 | |
583 | 56 | |
- | - | |
5.2 | 4.6 | |
5 months ago | about 2 years ago | |
Julia | Python | |
MIT License | MIT License |
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Tullio.jl
- A basic introduction to NumPy's einsum
- Generic GPU Kernels
-
Julia: Faster than Fortran, cleaner than Numpy
Julia ships with OpenBLAS, in some cases there are pure-Julia "blas-like" routine that can be as fast:
https://github.com/mcabbott/Tullio.jl
einop
- Einop: All einops operations via a single function
-
A basic introduction to NumPy's einsum
Somebody also realized that much of the time you can use one single function to describe all 3 of the einops operations. I present to you, einop: https://github.com/cgarciae/einop
What are some alternatives?
Zygote.jl - 21st century AD
einsum - Einstein Summation for Arrays in R
CUDA.jl - CUDA programming in Julia.
einops - Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
ForwardDiff.jl - Forward Mode Automatic Differentiation for Julia
NumPy - The fundamental package for scientific computing with Python.
TensorOperations.jl - Julia package for tensor contractions and related operations
array - C++ multidimensional arrays in the spirit of the STL
JuliaInterpreter.jl - Interpreter for Julia code
futhark - :boom::computer::boom: A data-parallel functional programming language
DaemonMode.jl - Client-Daemon workflow to run faster scripts in Julia
julia-numpy-fortran-test - Comparing Julia vs Numpy vs Fortran for performance and code simplicity