~upd~ | Cublaslt Grouped Gemm

// Allocate and fill matrices...

cublasLtGroupedMatmulPlan_t groupPlans[3]; for (int i = 0; i < groupCount; i++) { cublasLtGroupedMatmulPlanInit(handle, matmulDesc, &groupPlans[i], CUDA_R_16F, CUDA_R_16F, CUDA_R_16F, CUDA_R_32F, m_arr[i], n, k); }

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. cublaslt grouped gemm

cublasLtMatmulDesc_t matmulDesc; cublasLtMatmulDescCreate(&matmulDesc, CUDA_R_32F, CUDA_R_16F);

Enter – a modern solution designed to handle the messy, heterogeneous reality of advanced computing. The Problem with Traditional Batched GEMM Imagine training a recommendation system with embedding tables of varying sizes, or running inference on a transformer model with variable sequence lengths. In these scenarios, you might have 1,024 independent GEMM operations, each with different M, N, or K dimensions. // Allocate and fill matrices

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).

float alpha = 1.0f, beta = 0.0f; cublasLtMatmulGrouped(handle, nullptr, matmulDesc, &alpha, &beta, (void**)A_ptrs, (void**)B_ptrs, (void**)C_ptrs, (void**)C_ptrs, groupCount, groupPlans); cuBLASLt Grouped GEMM represents a paradigm shift for batched linear algebra on GPUs. It acknowledges that real-world workloads are irregular, heterogeneous, and dynamic. By moving the complexity of scheduling and fusing into the library, it allows developers to write clean, expressive code that still achieves near-peak hardware performance. If you're building a transformer-based model, a recommender

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 .