Matmul on Blackwell: Part 2 – Using Hardware Features to Optimize Matmul
5 days ago
- #GPU Optimization
- #Nvidia Blackwell
- #Matrix Multiplication
- Explained Nvidia's Blackwell GPU architecture and initial kernel performance issues.
- Introduced loop tiling and shared memory to optimize matrix multiplication (matmul).
- Detailed the use of Tensor Memory Accelerator (TMA) for asynchronous data transfer between global and shared memory.
- Explained the concept of core matrices and their role in tensor core operations.
- Introduced Tensor Memory (TMEM) and its advantages over register memory for storing matmul results.
- Described the process of transferring data from TMEM to registers and then to global memory.
- Explained shared memory bank conflicts and the use of swizzling to resolve them.
- Introduced the stmatrix instruction for packing output data in shared memory and using TMA store for efficient global memory writes.
- Highlighted performance improvements and remaining challenges, such as global memory access bottlenecks.
- Outlined future optimizations, including warp specialization and pipelining for further performance gains.