Oleksandr Pavlyk
Intel Corporation

Denis Nagorny
Intel Corporation

Andres Guzman-Ballen
Intel Corporation

Anton Malakhov
Intel Corporation

Hai Liu
Intel Corporation

Ehsan Totoni
Intel Corporation

Todd A. Anderson
Intel Corporation

Sergey Maidanov
Intel Corporation

Abstract

It is well-known that the performance difference between Python and basic C code can be up 200x, but for numerically intensive code another speed-up factor of 240x or even greater is possible. The performance comes from software's ability to take advantage of CPU's multiple cores, single instruction multiple data (SIMD) instructions, and high performance caches. The article describes optimizations, included in Intel® Distribution for Python*, aimed to automatically boost performance of numerically intensive code. This paper is intended for Python programmers who want to get the most out of their hardware but do not have time or expertise to re-code their applications using techniques such as native extensions or Cython.

Keywords

numpy,scipy,scikit-learn,numba,simd,parallel,optimization,performance

DOI

10.25080/shinma-7f4c6e7-00f

Bibtex entry

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Copyright © 2017 Oleksandr Pavlyk et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.