SwiftyWasmer
xtensor
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| SwiftyWasmer | xtensor | |
|---|---|---|
| 1 | 8 | |
| 40 | 3,205 | |
| - | 1.7% | |
| 0.4 | 7.9 | |
| over 3 years ago | 10 days ago | |
| Swift | C++ | |
| - | BSD 3-clause "New" or "Revised" License |
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SwiftyWasmer
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When researching and developing new algorithms to be used in the real-world production environment, what is your workflow and how do you usually do it? Do I have to prototype in Python, and then rewrite all code in C++/Rust?
One approach could be to run your Python algorithms inside Pyiodide inside a webassembly runtime like https://github.com/AlwaysRightInstitute/SwiftyWasmer . That would let you deploy the Python code that you did the research in. It depends which Python libraries you use though, I don't think pyiodide supports everything, but the big ones are there - numpy, scikit-learn, pillow etc
xtensor
- Does anyone know any good open source project to optimize?
- Container slicing in c++
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Which is the best way to work with matrices and linear algebra using c++?
I use xtensor: https://github.com/xtensor-stack/xtensor
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Can you give an example of well-designed C++ code, and explain why you think it is so?
Currently, one of my current favorites is xtensor.
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Risk of valarray being deprecated?
xTensor natively supports tensors and multidimensional arrays - not just vectors and matrices. Is fast, but not always as fast as Eigen and Blaze - and compiles very slow. Has a nice syntax.
- Xtensor: multi-dimensional arrays with broadcasting and lazy computing
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Eigen: A C++ template library for linear algebra
I wonder how Eigen compares to xtensor, which was inspired by Numpy and has support for views, slicing, and broadcasting?
https://github.com/xtensor-stack/xtensor
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When researching and developing new algorithms to be used in the real-world production environment, what is your workflow and how do you usually do it? Do I have to prototype in Python, and then rewrite all code in C++/Rust?
You can try eigen (http://eigen.tuxfamily.org/), armadillo (http://arma.sourceforge.net/) which is based on LAPACK which is what numpy is based on and xtensor (https://github.com/QuantStack/xtensor) which I think is the closest thing to numpy you’re gonna find in c++
What are some alternatives?
julia - The Julia Programming Language
Fastor - A lightweight high performance tensor algebra framework for modern C++
EA Standard Template Library - EASTL stands for Electronic Arts Standard Template Library. It is an extensive and robust implementation that has an emphasis on high performance.
dynarray - A header-only library, VLA for C++ (≥C++14). Extended version of std::experimental::dynarray
GLM - OpenGL Mathematics (GLM)
pinocchio - A fast and flexible implementation of Rigid Body Dynamics algorithms and their analytical derivatives
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
blis - BLAS-like Library Instantiation Software Framework
xtensor-blas - BLAS extension to xtensor
MLV-App - All in one MLV processing app.
DirectXMath - DirectXMath is an all inline SIMD C++ linear algebra library for use in games and graphics apps
vectormath - Modified version of Sony's open sourced vector and matrix math library.