bottleneck
pyxirr
| bottleneck | pyxirr | |
|---|---|---|
| 1 | - | |
| 1,006 | 146 | |
| 1.4% | - | |
| 3.5 | 8.3 | |
| 4 days ago | 3 months ago | |
| Python | Python | |
| BSD 2-clause "Simplified" License | Unlicense |
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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.
pyxirr
We haven't tracked posts mentioning pyxirr yet.
Tracking mentions began in Dec 2020.
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