This technique leverages a sophisticated feature of the CLC (Concurrent Launch Control) work-stealing mechanism to make grouped_gemm (grouped General Matrix Multiply) compatible with CUDA Graph.
Background
In high-performance computing (HPC) tasks, CUDA Graph reduces kernel launch overhead by recording and replaying workflows. However, combining grouped_gemm—which is crucial for architectures like Mixture-of-Experts (MoE)—with CUDA Graph is often challenging due to its dynamic scheduling nature.
Developments
The author shares how a specific characteristic of the CLC work-stealing mechanism keeps scheduling stable and predictable, thereby making it 'graphable'. This paves the way for deeper optimization of matrix multiplication kernels without sacrificing the flexibility of grouped_gemm.
Why It Matters
For AI and HPC engineers optimizing MoE models or large-scale inference systems, this is a crucial piece of the puzzle to squeeze maximum performance out of NVIDIA hardware. Reducing latency via CUDA Graph while maintaining grouped_gemm bandwidth is a highly valuable engineering goal.