Generating State-of-the-Art GEMMs with TorchInductor’s CuteDSL backend

Introduction TorchInductor currently supports three autotuning backends for matrix multiplications: Triton, CUTLASS (C++), and cuBLAS. This post describes the integration of CuteDSL as a fourth backend, the technical motivation for the work, and the performance results observed so far. The kernel-writing DSL space has gained significant momentum, with Triton, Helion, Gluon, CuTile, and CuteDSL each occupying a different point in the abstraction-performance tradeoff. When evaluating whether to integrate a new backend into TorchInductor, we apply three criteria: (1) the integration does not impose a large maintenance burden on our team, or there is a long-term committed effort from the vendor; (2) it does not regress compile time or benchmarking time relative to existing backends; and (3) it delivers better performance on target workloads. CuteDSL satisfies all three. NVIDIA is actively developing CuteDSL and provides optimized kernel templates, which limits the…