Beating NumPy's matrix multiplication in 150 lines of C code

This blog post by Aman Salykov explores implementing high-performance matrix multiplication in C code to outperform NumPy's matrix multiplication using OpenBLAS. The implementation follows the BLIS design, achieving over 1 TFLOPS on an AMD Ryzen 7700 CPU. The code is scalable, portable, and optimized for modern CPUs with FMA3 and AVX instructions. By parallelizing the code with OpenMP directives, it ensures scalability and performance. The post discusses the importance of matrix multiplication in neural networks and the role of BLAS libraries like OpenBLAS in optimizing linear algebra operations. It delves into the theoretical limits of CPU performance and presents a naive matrix multiplication implementation followed by optimizing it using kernels. The goal is to achieve high-performance matrix multiplication without delving into low-level assembly code. The post also references other tutorials on fast matrix multiplication and hints at a follow-up post on optimizing matrix multiplication on GPUs.