Cublaslt Grouped Gemm «RELIABLE ✭»

// Allocate and fill matrices...

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 . cublaslt grouped gemm

cublasLtMatmulDesc_t matmulDesc; cublasLtMatmulDescCreate(&matmulDesc, CUDA_R_32F, CUDA_R_16F); // Allocate and fill matrices

Traditional cuBLAS offers batched GEMM (e.g., cublas<t>gemmBatched ), which runs a list of independent matrix multiplications. However, it comes with a major limitation: (M, N, K) and data types. As NVIDIA continues to optimize cuBLASLt for Hopper

If you're building a transformer-based model, a recommender system, or any application that requires many small, independent matrix multiplications, Grouped GEMM should be your default choice. As NVIDIA continues to optimize cuBLASLt for Hopper and future architectures, the performance gap between irregular and regular workloads will only shrink further. For implementation details, refer to the NVIDIA cuBLASLt Developer Guide (CUDA 12.x and later).