Cython is a tool that allows you to write compiled extensions for Python by translating Python code into C or C++. It is commonly used to speed up software and is particularly useful for implementing small data science or scientific computing algorithms. However, there are ways to further optimize your code when Cython is too slow. In this article, the author focuses on using Single Instruction Multiple Data (SIMD) in the context of Cython. SIMD is a CPU feature that can execute the same operation on a sequence of primitive values using a single instruction, which can significantly improve speed. The author explores three ways to use SIMD in Cython: using intrinsics, enabling auto-vectorization, and using SIMD libraries. The article provides code examples and benchmarks to demonstrate the performance improvements achieved with these techniques. The author also discusses the challenges and considerations associated with each approach. Ultimately, the author suggests using a combination of techniques, such as enabling auto-vectorization and optimizing memory access patterns, to achieve the best performance in Cython. Overall, this article provides valuable insights and practical advice for optimizing low-level code in Cython.
https://pythonspeed.com/articles/faster-cython-simd/