In modern deep learning and HPC applications, workload shapes are rarely uniform. Standard approaches often struggle with these "ragged" batches:
Crucially , the standard "Grouped GEMM" usually refers to . cublaslt grouped gemm
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. In modern deep learning and HPC applications, workload
// For Batched/Grouped: Strides define the step to the next matrix in the group int64_t strideA = M * K; int64_t strideB = K * N; int64_t strideC = M * N; int batchCount = 100; // Number of GEMMs in the group it allows developers to write clean