Matrix Multiplication on Blackwell
First in a series on optimizing matrix multiplication for NVIDIA's Blackwell GPU. Covers architecture features (GB202/GB203 dies, new SM partitioning, enhanced Tensor Cores) and methodology using CUDA and low-level assembly tuning for peak performance.
Background
- The article discusses matrix multiplication, a fundamental math operation behind nearly all modern AI (neural networks, transformers, LLMs). Making this operation faster directly improves AI training and inference performance.
- "Blackwell" is NVIDIA's latest GPU architecture, announced in 2024 as the successor to Hopper (H100). It powers the B200 and GB200 chips used in data centers.
- NVIDIA's CUDA platform and its libraries (like CUTLASS) let developers write high-performance code for these GPUs. The article is aimed at engineers who optimize matrix multiplication on Blackwell hardware.
- Part 1 is introductory — it sets up the problem: how to best multiply two matrices on Blackwell to maximize use of the chip's math units (tensor cores) and memory bandwidth.
- This matters because companies racing to train and deploy larger AI models depend on squeezing every last bit of performance from NVIDIA's latest hardware.