In this blog post, the author shares their journey of implementing high-performance matrix multiplication on CPUs, focusing on simplicity, portability, and scalability. By following the BLIS design and utilizing OpenMP directives for parallelization, the code achieves over 1 TFLOPS peak performance on an AMD Ryzen 7700. The discussion covers the importance of matrix multiplication in neural networks, the role of BLAS libraries, and the development of optimized matmul algorithms. The comparison with NumPy showcases the theoretical limits of CPU performance and the strategies adopted to enhance speed. The post concludes with insights into kernel design and the importance of optimizing small sub-matrices for efficient matrix multiplication.
https://salykova.github.io/matmul-cpu