Accelerating Scientific Python with Intel Optimizations


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.