mlsanityData to 14 Oct 2026

AsmEvo: Agentic Assembly-Level Optimization of AMD GPU Kernels with Functional Equivalence Verification

arXiv 2608.20711 ↗First posted 21 Aug 2026cs.CLcited by 0

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Abstract (arXiv)

High-performance ML systems increasingly rely on GPU kernels whose editable source is unavailable, generated, or too distant from final machine code to expose remaining optimizations. Existing LLM kernel optimizers and autotuners mainly operate on CUDA, Triton, HIP, or tensor-program source and validate against reference implementations. We study a stricter setting: optimizing an already compiled AMDGPU code object, where the deployed binary is the only behavioral oracle. We present AsmEvo, an agentic assembly-level optimizer for AMD GPU kernels. Given an AMDGPU code object K0, AsmEvo reconstructs a reassemblable representation, proposes low-level edits with a long-horizon agent, rebuilds an ABI-preserving optimized object, and accepts candidates only after differential verification against K0 under identical launches. AsmEvo combines code-object recovery, metadata-aware rebuilding, profiling-guided hot-window editing, correctness-gated timing, and conservative in-place patch fallback. We conduct extensive experiments with AsmEvo on various AMD GPU kernels. On MI308X, AsmEvo improves 29 of 30 selected KernelBench kernels, reaching 1.35x geometric-mean and 3.88x maximum speedup. On MI300X production workloads, it improves all evaluated AITer binaries and vLLM/SGLang Triton assembly kernels, reaching 1.09x/1.31x and 1.18x/1.34x geometric-mean/maximum speedups, respectively, while preserving functional equivalence.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 29 references that are in the map. First posted on arXiv 21 Aug 2026.

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References · 15

  1. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,944
  2. 2023SGLang: Efficient Execution of Structured Language Model Programscited by 1,141
  3. 2025KernelBench: Can LLMs Write Efficient GPU Kernels?cited by 225
  4. 2020Ansor: Generating High-Performance Tensor Programs for Deep Learningcited by 201
  5. 2018Learning to Optimize Tensor Programscited by 140
  6. 2025TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operatorscited by 75
  7. 2025Towards Robust Agentic CUDA Kernel Benchmarking, Verification, and Optimizationcited by 56
  8. 2026CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generationcited by 47
  9. 2025CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimizationcited by 45
  10. 2025Geak: Introducing Triton Kernel AI Agent & Evaluation Benchmarkscited by 32
  11. 2022Tensor Program Optimization with Probabilistic Programscited by 30
  12. 2025EquiBench: Benchmarking Large Language Models' Reasoning about Program Semantics via Equivalence Checkingcited by 24
3 more
  1. 2026AVO: Agentic Variation Operators for Autonomous Evolutionary Searchcited by 24
  2. 2026Dr. Kernel: Reinforcement Learning Done Right for Triton Kernel Generationscited by 22
  3. 2017Souper: A Synthesizing Superoptimizercited by 12

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  1. 2026Ave: Guiding Agentic GPU Optimization Using Data-Flow Invariants
  2. 2026AMDKernelVault: Large-Scale Datasets and Agentic Training for AMD GPU Kernel Optimization
  3. 2026KernelOPT: Dispatch-Aware Agentic Search for GPU Kernel Optimization
  4. 2026FastKernels: Benchmarking GPU Kernel Generation in Production
  5. 2026A Two-Stage GPU Kernel Tuner Combining Semantic Refactoring and Search-Based Optimization
  6. 2026Agentic Kernel Optimization: Generating State-of-the-Art GPU Kernels Without Hand-Written CUDA
  7. 2026Improving Efficiency of GPU Kernel Optimization Agents using a Domain-Specific Language and Speed-of-Light Guidance
  8. 2025GPU Kernel Scientist: An LLM-Driven Framework for Iterative Kernel Optimization