FastKernels is a production-aligned benchmark covering 96.2% of HuggingFace Transformers that reveals state-of-the-art kernel agents deliver at most 0.94x aggregate speedup.
Towards robust agentic cuda kernel benchmarking, verification, and optimization
10 Pith papers cite this work. Polarity classification is still indexing.
10
Pith papers citing it
years
2026 10representative citing papers
CODA re-expresses most non-attention Transformer computations as GEMM-plus-epilogue programs using a constrained set of composable primitives to keep intermediate results on-chip and cut global memory traffic.
AgentKernelArena is a new open benchmark that measures complete AI agent workflows on 196 GPU kernel tasks with correctness, performance, and generalization checks to unseen configurations.
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.
Kernel Contracts is a specification language that formalizes correctness requirements for ML kernels to ensure consistent results across heterogeneous silicon platforms.
Coding-agent performance is workload- and framework-dependent, and raw speedup is an unsafe score because agents exploit benchmark-specific shortcuts.
KLineage derives verified optimization skills from backward lineages of expert GPU kernels to guide LLM agents toward higher-quality and more efficient kernels than memory-based baselines.
Presents Metal-Sci benchmark and harness for evolutionary LLM kernel search on Apple Silicon Metal, reporting in-distribution speedups up to 10.7x and using held-out gate scoring as oversight.
HTAM builds a Hierarchical Transition Graph to organize coarse global directions and detailed local strategies for guiding LLM-based CUDA kernel optimization, improving results on KernelBench.
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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FastKernels: Benchmarking GPU Kernel Generation in Production
FastKernels is a production-aligned benchmark covering 96.2% of HuggingFace Transformers that reveals state-of-the-art kernel agents deliver at most 0.94x aggregate speedup.
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CODA: Rewriting Transformer Blocks as GEMM-Epilogue Programs
CODA re-expresses most non-attention Transformer computations as GEMM-plus-epilogue programs using a constrained set of composable primitives to keep intermediate results on-chip and cut global memory traffic.
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AgentKernelArena: Generalization-Aware Benchmarking of GPU Kernel Optimization Agents
AgentKernelArena is a new open benchmark that measures complete AI agent workflows on 196 GPU kernel tasks with correctness, performance, and generalization checks to unseen configurations.
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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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PERFOPT-Bench: Evaluating Coding Agents on Software Performance Optimization
Coding-agent performance is workload- and framework-dependent, and raw speedup is an unsafe score because agents exploit benchmark-specific shortcuts.
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Learning When to Optimize: Verified Optimization Skills from Expert GPU-Kernel Lineages
KLineage derives verified optimization skills from backward lineages of expert GPU kernels to guide LLM agents toward higher-quality and more efficient kernels than memory-based baselines.
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Metal-Sci: A Scientific Compute Benchmark for Evolutionary LLM Kernel Search on Apple Silicon
Presents Metal-Sci benchmark and harness for evolutionary LLM kernel search on Apple Silicon Metal, reporting in-distribution speedups up to 10.7x and using held-out gate scoring as oversight.
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HTAM: Hierarchical Transition-Attended Memory for Operator Optimization
HTAM builds a Hierarchical Transition Graph to organize coarse global directions and detailed local strategies for guiding LLM-based CUDA kernel optimization, improving results on KernelBench.
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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.