FACT: Compositional Kernel Synthesis with a Three-Stage Agentic Workflow
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866911645682368512 |
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| author | Heidari, Sina Nikolopoulos, Dimitrios S. |
| author_facet | Heidari, Sina Nikolopoulos, Dimitrios S. |
| contents | Deep learning compilers and vendor libraries deliver strong baseline performance but their performance is 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 three-stage agent-driven workflow optimizing PyTorch modules through multi-pattern composition while grounding synthesis in CUTLASS C++. Pattern discovery inspects the traced graph, matches subgraphs to optimization rules, retrieves vetted examples, and outputs prioritized patterns. Pattern realization implements each pattern as a CUTLASS kernel, verifies, and auto-tunes. Pattern composition assembles extensions into an optimized module for benchmarking. We evaluate the workflow on KernelBench across NVIDIA A100 and H100 GPUs. On Level 1 GEMM problems (square, batched, large-K matrix multiply), auto-tuned CUTLASS kernels achieve 1.06x-1.18x speedups on A100 and 0.84x-1.80x performance variations on H100 over cuBLAS. On Level 3 transformer blocks against PyTorch eager baseline, FACT achieves 2.03x speedup on MiniGPT (vs. Inductor: 1.89x, TensorRT: 1.85x) and 1.41x on Llama 3 8B (vs. Inductor: 1.17x, TensorRT: 1.18x). Our framework couples agentic graph-level pattern discovery with architecture-specific auto-tuning and a dynamic pattern registry, offering a practical path from traced PyTorch modules to deployable kernels. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_26666 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | FACT: Compositional Kernel Synthesis with a Three-Stage Agentic Workflow Heidari, Sina Nikolopoulos, Dimitrios S. Distributed, Parallel, and Cluster Computing Performance Deep learning compilers and vendor libraries deliver strong baseline performance but their performance is 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 three-stage agent-driven workflow optimizing PyTorch modules through multi-pattern composition while grounding synthesis in CUTLASS C++. Pattern discovery inspects the traced graph, matches subgraphs to optimization rules, retrieves vetted examples, and outputs prioritized patterns. Pattern realization implements each pattern as a CUTLASS kernel, verifies, and auto-tunes. Pattern composition assembles extensions into an optimized module for benchmarking. We evaluate the workflow on KernelBench across NVIDIA A100 and H100 GPUs. On Level 1 GEMM problems (square, batched, large-K matrix multiply), auto-tuned CUTLASS kernels achieve 1.06x-1.18x speedups on A100 and 0.84x-1.80x performance variations on H100 over cuBLAS. On Level 3 transformer blocks against PyTorch eager baseline, FACT achieves 2.03x speedup on MiniGPT (vs. Inductor: 1.89x, TensorRT: 1.85x) and 1.41x on Llama 3 8B (vs. Inductor: 1.17x, TensorRT: 1.18x). Our framework couples agentic graph-level pattern discovery with architecture-specific auto-tuning and a dynamic pattern registry, offering a practical path from traced PyTorch modules to deployable kernels. |
| title | FACT: Compositional Kernel Synthesis with a Three-Stage Agentic Workflow |
| topic | Distributed, Parallel, and Cluster Computing Performance |
| url | https://arxiv.org/abs/2604.26666 |