Cublaslt Grouped Gemm Documentation |link|

#CUDA #cuBLASLt #GPUComputing #GEMM #LLM #PerformanceOptimization

To execute a grouped GEMM, the user typically provides arrays of pointers to the matrices: cublaslt grouped gemm documentation

If you're working with (e.g., in LLM inference, attention mechanisms, or recommendation systems), you’ve likely hit the overhead of launching many separate GEMM kernels. It is clear that even with millions of

2.1. ... It is clear that even with millions of small independent matrices we will not be able to achieve the same GFLOPS rate as ... NVIDIA Docs What are the key differences between batched matrix ... Traditional matrix multiplications, such as single large GEMM operations, are optimized for handling one large matrix multiplicati... Massed Compute cuBLAS Library - NVIDIA Documentation The cuBLASLt is a lightweight library dedicated to GEneral Matrix-to-matrix Multiply (GEMM) operations with a new flexible API. Th... NVIDIA Docs Accelerating MoE's with a Triton Persistent Cache-Aware Grouped ... Aug 18, 2025 — Massed Compute cuBLAS Library - NVIDIA Documentation The

For users requiring even more control, NVIDIA's (which often powers cuBLAS kernels) uses a grouped kernel scheduler. This scheduler assigns work to threadblocks in a round-robin fashion, ensuring that even if some GEMMs in your group are significantly larger than others, the GPU's Streaming Multiprocessors (SMs) remain balanced.