pyparallel.org

PyParallel

An experimental, proof-of-concept fork of Python 3. Designed to optimally exploit multiple CPU cores, fast. SSDs, NUMA architectures and 10Gb Ethernet networks. Of the Global Interpreter Lock (GIL). Without needing to remove it at all. Async I/O meets multicore. PyParallel combines the concurrency benefits afforded by single-threaded async I/O. Architectures with the performance benefits afforded by simultaneous multi-threading. Scales linearly with cores and I/O bandwidth. Efficiently from multiple cores.

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PyParallel | pyparallel.org Reviews

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An experimental, proof-of-concept fork of Python 3. Designed to optimally exploit multiple CPU cores, fast. SSDs, NUMA architectures and 10Gb Ethernet networks. Of the Global Interpreter Lock (GIL). Without needing to remove it at all. Async I/O meets multicore. PyParallel combines the concurrency benefits afforded by single-threaded async I/O. Architectures with the performance benefits afforded by simultaneous multi-threading. Scales linearly with cores and I/O bandwidth. Efficiently from multiple cores.

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PyParallel | pyparallel.org Reviews

https://pyparallel.org

An experimental, proof-of-concept fork of Python 3. Designed to optimally exploit multiple CPU cores, fast. SSDs, NUMA architectures and 10Gb Ethernet networks. Of the Global Interpreter Lock (GIL). Without needing to remove it at all. Async I/O meets multicore. PyParallel combines the concurrency benefits afforded by single-threaded async I/O. Architectures with the performance benefits afforded by simultaneous multi-threading. Scales linearly with cores and I/O bandwidth. Efficiently from multiple cores.

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faster-cpython.readthedocs.io

Projects to optimize CPython 3.7 — Faster CPython 0.0 documentation

http://faster-cpython.readthedocs.io/cpython37.html

Projects to optimize CPython 3.7. Projects to optimize CPython 3.6. Notes on Python and CPython performance, 2017. Everything in Python is mutable. Register-based Virtual Machine for Python. History of Python optimizations. Random notes about PyPy. Projects to optimize CPython 3.7. Projects to optimize CPython 3.7. See also Projects to optimize CPython 3.6. Multiple interepters per process. 8220;solving multi-core Python”. Https:/ mail.python.org/pipermail/python-ideas/2015-June/034177.html. Issue #11549...

speakerdeck.com

PyParallel: How we removed the GIL and exploited all cores // Speaker Deck

https://speakerdeck.com/trent/pyparallel-how-we-removed-the-gil-and-exploited-all-cores

PyParallel: How we removed the GIL and exploited all cores. November 11, 2013. Without needing to remove the GIL at all. Presentation I gave at PyData NYC 2013. Video of the presentation is here: http:/ vimeo.com/79539317. Article by InfoQ: http:/ www.infoq.com/articles/PyParallel. Reddit thread: http:/ www.reddit.com/r/programming/comments/1qrnew/pyparallel how we removed the gil and exploited/. Hacker News thread: https:/ news.ycombinator.com/item? Http:/ www.continuum.io. Stats 50,511 Views.

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pyparallel.org

PyParallel

An experimental, proof-of-concept fork of Python 3. Designed to optimally exploit multiple CPU cores, fast. SSDs, NUMA architectures and 10Gb Ethernet networks. Of the Global Interpreter Lock (GIL). Without needing to remove it at all. Async I/O meets multicore. PyParallel combines the concurrency benefits afforded by single-threaded async I/O. Architectures with the performance benefits afforded by simultaneous multi-threading. Scales linearly with cores and I/O bandwidth. Efficiently from multiple cores.

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