KernelBench
PulseAugur coverage of KernelBench — every cluster mentioning KernelBench across labs, papers, and developer communities, ranked by signal.
4 day(s) with sentiment data
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ByteDance and Tsinghua AIR train LLMs to write faster GPU code with CUDA Agent
ByteDance Seed and Tsinghua AIR have developed CUDA Agent, a system that uses reinforcement learning to train large language models to generate optimized GPU kernels. This system achieved a 98.8% correctness rate and ge…
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New frameworks optimize GPU kernels for deep learning and HPC · 3 sources tracked
Three new research papers introduce advanced frameworks for optimizing GPU kernels, crucial for deep learning and high-performance computing. HIERA focuses on workload-aware planning across different implementation spac…
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LLM framework AUTO automates GPU code optimization, beats baselines
Researchers have developed AUTO, a novel framework that leverages large language models (LLMs) for automated design optimization. This system employs a Strategist agent for high-level planning and multiple Implementor a…
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StitchCUDA framework automates end-to-end GPU programming with multi-agent RL
Researchers have developed StitchCUDA, a novel multi-agent framework designed for end-to-end GPU program generation. This system employs specialized agents for planning, coding, and verification to optimize machine lear…
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JAXBench launches to optimize AI kernels on Google TPUs
A new benchmark suite called JAXBench has been developed to specifically address the optimization of AI kernel performance on Google Cloud TPUs. This suite includes 50 JAX workloads derived from prominent AI models like…
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LLM-generated kernels show inflated performance in benchmarks, new study finds
A new research paper introduces KernelBench-Verified, an enhanced evaluation framework designed to more accurately assess the performance of LLM-generated CUDA kernels. The study highlights that current evaluation metho…
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LLMs struggle with GPU kernel generation; new research offers solutions
Two new research papers explore the challenges of generating correct GPU kernels using large language models (LLMs). The first paper, "The Correctness Illusion in LLM-Generated GPU Kernels," identifies that existing ben…
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daVinci-kernel uses RL to optimize GPU kernels with evolving skill library
Researchers have developed daVinci-kernel, a novel reinforcement learning framework designed to optimize GPU kernels. This system co-evolves skill selection, summarization, and utilization, employing three agents that s…
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AI benchmarks hardened against reward hacking with adversarial loops
Researchers have developed a novel "hacker-fixer loop" to improve the robustness of AI agent benchmarks against reward hacking. This adversarial process uses three LLM agents to iteratively identify and patch vulnerabil…
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LLM framework automates GPU kernel generation, outperforming PyTorch
Researchers have developed CuTeGen, a new framework designed to automate the creation and optimization of high-performance GPU kernels. This agentic system employs a structured workflow of generating, testing, and refin…
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MusaCoder framework achieves state-of-the-art in GPU kernel generation
Researchers have developed MusaCoder, a novel framework for generating native GPU kernels, which are essential for efficient low-level code execution. This system employs a full-stack training approach, integrating data…
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Kernel Foundry optimizes GPU kernels using evolutionary search and LLMs
Researchers have developed Kernel Foundry, an evolutionary framework designed to optimize GPU kernels for both correctness and performance. This system leverages large language models for initial code generation, then r…
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New HTAM Framework Optimizes GPU Kernels for LLMs
Researchers have introduced HTAM, a novel framework designed to optimize GPU kernels for large language models. HTAM addresses the challenge of granularity mismatch in LLM-based code generation by organizing optimizatio…