arXiv AI
Ave: Guiding Agentic GPU Optimization Using Data-Flow Invariants
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KernelOPT: Dispatch-Aware Agentic Search for GPU Kernel Optimization
KernelOPT is a multi‑agent system that optimizes GPU kernels generated by compilers like PyTorch Inductor by treating compiled models as structured artifacts. It preserves vendor library calls and focuses on Triton sub‑kernels, using five profiling‑guided LLM agents and a four‑gate verification cascade to ensure correctness and performance before re‑stitching the model. On 250 KernelBench problems, KernelOPT achieves geometric mean speedups of 1.40×, 1.15×, and 1.07× over torch.compile at three optimization levels.
Hugging Face Trending Papers
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KernelOPT: Dispatch-Aware Agentic Search for GPU Kernel Optimization
KernelOPT is a multi-agent system that optimizes GPU kernels generated by compilers like PyTorch Inductor by treating compiled models as structured artifacts. It preserves vendor library calls and focuses on Triton sub-kernels, using five profiling-guided LLM agents and a four-gate verification cascade to filter and validate candidates. On 250 KernelBench problems, KernelOPT achieves geometric mean speedups of 1.40×, 1.15×, and 1.07× over torch.compile at different optimization levels.
arXiv AI
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Are LLM-Generated GPU Kernels Production-Ready? A Trace-Driven Benchmark and Optimization Agent
arXiv:2607. 14541v1 Announce Type: new Abstract: Existing GPU kernel generation benchmarks draw problems from synthetic or curated sources that diverge from deployed workloads.
arXiv AI
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Kernel Forge: An Agent Harness for LLM-based Generation and Optimization of CUDA Kernels
arXiv:2607. 24762v1 Announce Type: new Abstract: Machine learning models are increasingly embedded in everyday software, and most of their runtime is spent in a small set of compute kernels such as matrix multiplication, convolution, and normalization.
arXiv Machine Learning
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CAKE: Compiler-Agent Co-Design for Frontier Kernel Evolution
arXiv:2608. 12629v1 Announce Type: new Abstract: GPU kernel agents and GPU programming languages have advanced separately, leaving expert kernels difficult to reproduce.
arXiv Computation and Language
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AsmEvo: Agentic Assembly-Level Optimization of AMD GPU Kernels with Functional Equivalence Verification
AsmEvo is an agentic assembly-level optimizer that targets compiled AMDGPU code objects, reconstructing a reassemblable representation and applying low-level edits guided by a long-horizon agent. It rebuilds ABI-preserving optimized objects and verifies functional equivalence through differential testing against the original binary. Experiments show significant speedups—up to 1.35× geometric mean on MI308X and 1.18× on MI300X—while maintaining correctness.