high performance computing on graphics processing units: hgpu.org

Deep learning compilers and vendor libraries deliver strong baseline performance but are bounded by finite, engineer-curated catalogs. When these omit needed optimizations, practitioners substitute hand-written CUDA or CUTLASS, demanding expertise in GPU microarchitecture and C++ template metaprogramming. Recent LLM-based agents target kernel generation in raw CUDA, forcing rediscovery of optimizations already encoded in mature libraries. We present FACT (Framework for Agentic CUTLASS Transpilation), a framework that employs a three-stage, agent-driven workflow optimizing PyTorch modules through multi-pattern composition while grounding synthesis in CUTLASS C++. (1) Pattern discovery: an LLM agent inspects the traced graph, matches subgraphs to optimization rules, retrieves vetted examples from an architecture-specific index, and outputs prioritized patterns. (2) Pattern realization: each pattern is implemented as a CUTLASS kernel wrapped in a PyTorch extension, verified, and auto-tuned by sweeping parameters inferred from the CUTLASS hierarchy. (3) Pattern composition: extensions are loaded together into a single composed module for end-to-end benchmarking. We evaluate the workflow using KernelBench’s evaluation framework and provided modules on an NVIDIA A100. On Level 1, we apply the workflow to three GEMM workloads (square matrix multiply, batched matrix multiply, and large-K matrix multiply). Auto-tuned CUTLASS kernels improve over PyTorch cuBLAS baseline by 1.06x-1.18x. On Level 3 MiniGPT block, composing fused multi-head attention with fused MLP GEMM+GELU yields 2.79x end-to-end speedup. Our work couples agentic graph-level pattern discovery with auto-tuning and a dynamic pattern table, offering a practical path from traced PyTorch to deployable kernels by automating CUTLASS kernel synthesis and auto-tuning.