pyxirr
bottleneck
pyxirr | bottleneck | |
---|---|---|
- | 1 | |
146 | 1,006 | |
- | 1.4% | |
8.3 | 3.5 | |
3 months ago | 7 days ago | |
Python | Python | |
Unlicense | BSD 2-clause "Simplified" License |
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pyxirr
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Tracking mentions began in Dec 2020.
bottleneck
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Update on my Python, C++ and Rust Library
Fast Array Manipulation in Python: Since Numpy is the de facto standard for storing multi-dimensional data, any performance gain you see using librapid math kernels will need to be realized on data which probably started its life as a numpy array, and needs to be passed to another tool as a numpy array. Hopefully there will be (or already is?) a way to build a librapid array out of a numpy array without copying the data and vice versa. In fact I might suggest that librapid focus on the fast math operations and simply become an accelerator for numpy arrays. For instance, look at CuPy which provides GPU-implemented operations within a numpy-compatible API, and Bottleneck which simply provides fast C-based implementations of some otherwise slow parts of Numpy. Also note that numpy *can* be multi-threaded depending on the operation and some environment variables. Single-threaded to Single-threaded I think you will be hard-pressed to beat Numpy on general math operations, but that doesn't mean there aren't specific "kernels" that are more specialized that can be greatly improved with a C++ back-end.
What are some alternatives?
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