Unweaving warp specialization on modern tensor core GPUs
5 hours ago
- #High Performance Computing
- #GPU
- #Warp Specialization
- Warp specialization is a technique used to optimize high-performance kernels for modern Tensor Core GPUs like NVIDIA’s H100 and B200.
- GPUs consist of streaming multiprocessors (SMs) that execute threads grouped into warps, which operate in a SIMT (Single Instruction, Multiple Threads) model.
- Warp specialization helps mitigate performance degradation caused by thread divergence within warps by assigning different tasks to different warps.
- Examples of warp specialization include CUDA-DMA (separating memory loading and computation) and Singe (partitioning computations to bypass register limits).
- Warp specialization is particularly useful for Tensor Core and Tensor Memory Accelerator (TMA) operations on Hopper and Blackwell GPUs.
- Three key scenarios where warp specialization is beneficial: resource constraints, variable-latency operations, and blocking synchronization.
- Testing showed that in some cases, like GEMM on H100, high performance can be achieved without warp specialization through careful loop reordering.
- Warp specialization represents a trade-off between implementation complexity and performance, with human effort and compiler capabilities playing key roles.
- Future directions may include hardware improvements, better compiler algorithms, or systems software to simplify warp-specialized code.