Peter Zhang
Sep 02, 2025 17:59
NVIDIA introduces nvMatmulHeuristics to streamline GEMM kernel tuning, decreasing time and enhancing efficiency on GPUs, built-in with CUTLASS 4.2.
NVIDIA has unveiled a brand new strategy to optimize Common Matrix Multiplication (GEMM) kernel tuning on its GPUs, addressing the challenges confronted by builders in choosing optimum configurations. The introduction of nvMatmulHeuristics, a GPU kernel meta-parameter optimization module, goals to streamline the method by using quick heuristics, considerably decreasing the time required for kernel tuning, based on NVIDIA’s official weblog.
Challenges in GEMM Kernel Optimization
GEMM kernel efficiency is influenced by quite a few compile-time and runtime meta-parameters, comparable to CTA, warp and instruction-level tile sizes, kernel schedules, and extra. Historically, discovering the optimum kernel requires producing and compiling 1000’s of potential configurations, adopted by exhaustive auto-tuning, which might be time-consuming and cumbersome.
Introducing nvMatmulHeuristics
To alleviate these challenges, NVIDIA has developed nvMatmulHeuristics, which offers a streamlined workflow for GEMM kernel tuning. This module analyzes the particular parameters of an operation and the capabilities of the goal {hardware} to counsel a restricted set of optimum kernel configurations, enhancing efficiency whereas decreasing tuning time.
Built-in with CUTLASS 4.2, nvMatmulHeuristics simplifies the method by predicting a small, focused set of high-potential kernel configurations, thus reworking the kernel technology and tuning course of. This integration permits builders to rapidly determine top-performing candidates with out resorting to exhaustive search strategies.
Effectivity Features with Heuristic-Primarily based Tuning
The heuristic strategy includes a three-step course of: heuristic prediction, kernel technology, and auto-tuning. By specializing in a small variety of promising configurations, the time required to discover a high-performance kernel is dramatically decreased. This technique not solely saves time but additionally permits builders to attain near-optimal efficiency effectively.
The impression of nvMatmulHeuristics is obvious in efficiency testing. On NVIDIA’s H100 SXM GPU, the module achieved 96% of peak efficiency in simply 150 minutes, in comparison with over 700 minutes required by an exhaustive search. Equally, on the NVIDIA B200 GPU, it reached 99% of peak efficiency with a greater than 5x speedup in construct and tuning time.
Availability and Future Implications
nvMatmulHeuristics is now out there in early entry, offering help for varied GPU architectures, together with NVIDIA Ampere, Ada, Hopper, and preliminary Blackwell architectures. It accommodates all Tensor Core-based GEMM precisions and gives each Python and C++ APIs for builders.
By enabling quicker and extra environment friendly kernel tuning, nvMatmulHeuristics has the potential to boost productiveness throughout deep studying frameworks, compilers, and kernel libraries. This development represents a big step ahead in optimizing GPU efficiency for complicated computational duties.
Picture supply: Shutterstock