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01- 11 2019

Scipy and scikit-learn compiled to WebAssembly in Pyodide

The Pyodide project aims to compile the CPython interpreter and scientific Python stack to WebAssembly allowing to use Python libraries in the browser. In the past, numpy, pandas and matplotlib have been packaged in Pyodide. In this post we will outline latest developments funded by Nexedi, which include packaging scipy and scikit-learn, improvements in testing workflow and ability to install packages from custom URLs.
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01- 11 2019

Cython nogil extension on multi core introduction

In this blog post, we show a proof of concept to make multi-core processing possible in CPython. It involves adding a new data type in Cython which is not limited by the CPython GIL and a new coroutine which together make real multi-core possible.
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07- 05 2018

A multi-core Python HTTP server (much) faster than Go (spoiler: Cython)

A multi-core Python HTTP server that is about 40% to 110% faster than Go can be built by relying on the Cython language and LWAN C library. A proof of concept validates the possibility of high performance system programming in the Cython language.
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07- 05 2018

Python multi-core benchmark in uvloop context

The existence of cython combined with a clean concurrency model based on technologies such as gevent and pygolang could change the situation if both can be tightly integrated into cython's static cdef code rather than scattered as it is today.
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02- 01 2018

Cython without GIL

Nexedi is looking for a python and C developer interested in implementing a multi-threaded coroutine and garbage collector extension for the Cython language.
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08- 12 2017

High Peformance Multi-core Python at Nexedi

Nexedi has been able to use python language in a multi-core high performance fashion for over 10 years. We explain how in this blog and provide a simple set of rules to quickly remember how and why python is suitable for concurrent programming. We also explain our decision to use golang in rare cases where python is not suitable.
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06- 12 2017

Improving python memory management in a multi-process environment with wendelin.core

This talk explains how to efficiently use memory with mutli-process deployments of cpython on multi-core servers or clusters, thanks to a distributed transactional memory manager: wendelin.core.
0 comment | Share: Facebook logo Twitter Logo | Categories python ; wendelin ; big data ; memory ; cython ; process ; thread