Beating NumPy's matrix multiplication in 150 lines of C code
Aman Salykov's blog delves into high-performance matrix multiplication in C, surpassing NumPy with OpenBLAS on AMD Ryzen 7700 CPU. Scalable, portable code with OpenMP, targeting Intel Core and AMD Zen CPUs. Discusses BLAS, CPU performance limits, and hints at GPU optimization.
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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 parallelized with OpenMP directives. It targets modern Intel Core and AMD Zen CPUs but requires fine-tuning for optimal performance. The post discusses the importance of matrix multiplication in neural networks, the use of BLAS libraries like OpenBLAS, and the theoretical limits of CPU performance using SIMD extensions like AVX and FMA. It compares a naive matrix multiplication implementation in C to optimized kernel-based approaches used in libraries like BLIS. The post provides insights into optimizing matrix multiplication for different CPU architectures and hints at a follow-up post on optimizing matrix multiplication for GPUs.
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