Cublaslt Grouped Gemm [BEST]

void* A_ptrs[3]; void* B_ptrs[3]; void* C_ptrs[3];

Deep Learning Recommendation Models (DLRM) have embedding tables of different sizes followed by MLPs. Grouped GEMM executes all the per-embedding MLP forward passes simultaneously. cublaslt grouped gemm

In the world of High-Performance Computing (HPC) and Deep Learning (DL), the General Matrix Multiply (GEMM) operation is the undisputed king. From large language models (LLMs) to scientific simulations, performance often hinges on how efficiently you can compute C = α*A*B + β*C . removes this restriction

), removes this restriction, enabling developers to process "irregular" workloads—such as those found in Mixture-of-Experts (MoE) models or LoRA (Low-Rank Adaptation) fine-tuning—with significantly higher GPU efficiency. Why Grouped GEMM? cublaslt grouped gemm