KernelBenchX benchmark shows task category explains nearly three times more variance in LLM kernel correctness than method choice, iterative refinement boosts correctness but reduces performance, and quantization remains unsolved.
Towards robust agentic cuda kernel benchmarking, verification, and optimization.arXiv preprint arXiv:2509.14279
4 Pith papers cite this work. Polarity classification is still indexing.
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Kernel Contracts is a specification language that formalizes correctness requirements for ML kernels to ensure consistent results across heterogeneous silicon platforms.
Metal-Sci is a benchmark and harness for LLM evolutionary optimization of Apple Silicon Metal kernels that uses held-out sizes to detect silent regressions missed by in-distribution scores.
KEET uses LLM agents to generate data-grounded natural language explanations of performance issues in GPU kernels from Nsight Compute profiles and shows these improve downstream LLM-based optimization tasks.
citing papers explorer
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KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels
KernelBenchX benchmark shows task category explains nearly three times more variance in LLM kernel correctness than method choice, iterative refinement boosts correctness but reduces performance, and quantization remains unsolved.
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Kernel Contracts: A Specification Language for ML Kernel Correctness Across Heterogeneous Silicon
Kernel Contracts is a specification language that formalizes correctness requirements for ML kernels to ensure consistent results across heterogeneous silicon platforms.
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Metal-Sci: A Scientific Compute Benchmark for Evolutionary LLM Kernel Search on Apple Silicon
Metal-Sci is a benchmark and harness for LLM evolutionary optimization of Apple Silicon Metal kernels that uses held-out sizes to detect silent regressions missed by in-distribution scores.
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KEET: Explaining Performance of GPU Kernels Using LLM Agents
KEET uses LLM agents to generate data-grounded natural language explanations of performance issues in GPU kernels from Nsight Compute profiles and shows these improve downstream LLM-based optimization tasks.