In a standard GEMM, you multiply $A \times B = C$. If you have 1,000 small matrices to multiply (e.g., dimensions $64 \times 64$), launching 1,000 separate kernels is inefficient. The GPU spends more time scheduling kernels than executing math.
Enter – a modern solution designed to handle the messy, heterogeneous reality of advanced computing.
While standard grouped GEMMs in NVIDIA Docs improve efficiency by launching multiple operations in one kernel, they often suffer from when one large GEMM is grouped with many tiny ones. A load-balanced scheduler would dynamically reassign thread blocks across the GPU to ensure that all Streaming Multiprocessors (SMs) finish their work at roughly the same time. Why this is a "Good Feature":
In a standard GEMM, you multiply $A \times B = C$. If you have 1,000 small matrices to multiply (e.g., dimensions $64 \times 64$), launching 1,000 separate kernels is inefficient. The GPU spends more time scheduling kernels than executing math.
Enter – a modern solution designed to handle the messy, heterogeneous reality of advanced computing.
While standard grouped GEMMs in NVIDIA Docs improve efficiency by launching multiple operations in one kernel, they often suffer from when one large GEMM is grouped with many tiny ones. A load-balanced scheduler would dynamically reassign thread blocks across the GPU to ensure that all Streaming Multiprocessors (SMs) finish their work at roughly the same time. Why this is a "Good Feature":