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PNumPy\nParallel NumPy seamlessly speeds up NumPy for large arrays (64K+ elements) with *no change required to your existing NumPy code*.\n\nPNumPy supports Linux, Windows, and MacOS for NumPy \u003e= 1.18 for python 3.6, 3.7, 3.8, and 3.9.\n\nThis first release speeds up NumPy binary and unary ufuncs such as **add, multiply, isnan, abs, sin, log, sum, min and many more**.\nSped up functions also include: **sort, argsort, lexsort, arange, boolean indexing, and fancy indexing**.\nIn the near future we will speed up: **astype, where, putmask, and searchsorted**.\n\nOther packages that use numpy, such as [scikit-learn](https://scikit-learn.org/stable/) or [pandas](https://github.com/pandas-dev/pandas), will also be sped up for large arrays.\n\n[![CI Status](https://github.com/Quansight/pnumpy/workflows/tox/badge.svg)](https://github.com/Quansight/pnumpy/actions)\n\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n\n## Installation\n```\npip install pnumpy\n```\n\nYou can also install the latest development versions with\n```\npip install https://github.com/Quansight/pnumpy/archive/main.zip\n```\n\n## Documentation\n\nSee the [full documentation](https://quansight.github.io/pnumpy/stable/index.html)\n\nTo use the project:\n\n```python\nimport pnumpy as pn\n```\n\nParallel NumPy speeds up NumPy silently under the hood.  To see some benchmarks yourself run\n```\npn.benchmark()\n```\n![plot](./doc_src/images/bench4graph2.PNG)\n![plot](./doc_src/images/bench4graph3.PNG)\n\nTo get a partial list of functions sped up run\n```\npn.atop_info()\n```\n\nTo disable or enable pnumpy run\n```\npn.disable()\npn.enable()\n```\n\n## Additional Functionality\nPNumPy provides additional routines such as converting a NumPy record array to a column major array in parallel (**pn.recarray_to_colmajor**) which is useful for DataFrames.  Other routines include **pn.lexsort32**, which performs an indirect sort using **np.int32** instead of **np.int64** consuming half the memory and running faster.\n\n## Threading\nPNumPy uses a combination of threads and 256 bit vector intrinsics to speed up calculations.  By default most operations will only use 3 additional worker threads in combination with the main python thread for a total 4.  Large arrays are divided up into 16K chunks and threads are assigned to maintain cache coherency.  More threads are dynamically deployed for more intensive CPU problems like **np.sin**.  Users can customize threading.  The example below shows how 4 threads can work together to quadruple the effective L2 cache size.\n\n![plot](./doc_src/images/threading_npadd.PNG)\n\nTo cap the number of additional worker threads to 3 run\n```\npn.thread_setworkers(3)\n```\n\nTo disable or re-enable threading run\n```\npn.thread_disable()\npn.thread_enable()\n```\n\nTo disable or re-enable just the atop engine run\n```\npn.atop_disable()\npn.atop_enable()\n```\n\n## FAQ\n**Q: If I type np.sort(a) where a is an array, will it be sped up?**\n\n*A: If len(a) \u003e 65536 and pnumpy has been imported, it will automatically be sped up*\n\n**Q: How is sort sped up?**\n\n*A: PNumPy uses additional threads to divide up the sorting job.  For example it might perform an 8 way quicksort followed by a 4 way mergesort*\n\n**Q: How is scikit or pandas sped up?**\n\n*A: PNumPy's vector loops and threads will speed up any package that uses large NumPy arrays*\n\n## Development\n\nTo run all the tests run:\n\n```\npython -m pip install pytest\npython -m pytest tests\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fquansight%2Fpnumpy","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fquansight%2Fpnumpy","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fquansight%2Fpnumpy/lists"}