Matrix Multiplication on Blackwell (www.modular.com)

The latest blog series focuses on developing high-performance GPU kernels for matrix multiplication (matmul) on NVIDIA's Blackwell architecture, aiming to achieve performance comparable to NVIDIA's cuBLAS library. Part 1 introduces the significance of matmul in large language models (LLMs) such as ChatGPT and Google’s Gemini, where over 83% of runtime is spent on matmul operations. By optimizing matmul performance, even a modest 10% improvement can yield an approximate 8% end-to-end speedup, translating directly into substantial cost savings for organizations heavily investing in AI systems. The series emphasizes the unique features of Blackwell GPUs, particularly the introduction of fifth-generation tensor cores, which enable larger sub-matrix multiplications (up to 256x256x16). This advancement enhances the computing throughput required for extensive matmul operations—a critical aspect of linear algebra underpinning many AI methodologies. Upcoming parts will incrementally showcase how to leverage Blackwell's capabilities and hardware instructions to continuously enhance matmul performance, making it an essential reference for developers looking to optimize their GPU programming and improve computational efficiency for AI/ML applications.