Cublaslt Grouped Gemm ❲UHD 2027❳

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.

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); } cublaslt grouped gemm

cublasLtMatmulDesc_t matmulDesc; cublasLtMatmulDescCreate(&matmulDesc, CUDA_R_32F, CUDA_R_16F); Enter – a modern solution designed to handle

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). cublasLtGroupedMatmulPlan_t groupPlans[3]; for (int i = 0; i

// Allocate and fill matrices...

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.

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.