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KernelArc: Multi-Agent Framework Optimizes GPU Kernels Autonomously

Joyjit Kundu, Ben Stoffelen, Kaili Wang, Peter Vrancx, Ludovic Denoyer· August 19, 2026 View original

Key takeaways

  • KernelArc is a multi-agent framework for autonomous GPU kernel optimization.
  • It uses specialized agents, shared memory, and a benchmark guard for coordination.
  • The framework achieved top rankings on NVIDIA H100 and B200 GPUs for various tasks.
  • Multi-agent search broadens exploration and leads to stronger optimization results.

Who benefits

Cloud ComputingAI DevelopmentScientific ResearchGamingAutomotive

Summary

KernelArc is a new multi-agent framework designed for autonomous GPU kernel optimization across diverse workloads. It uses specialized agents that coordinate through shared memory and a benchmark guard to achieve state-of-the-art performance on various tasks.

Optimizing GPU kernels for diverse computational workloads is a complex and time-consuming task, often requiring deep expertise. A novel multi-agent framework, KernelArc, has been developed to automate this process. This system employs several specialized agents that operate in parallel, each focusing on specific optimization strategies. These agents coordinate their efforts by sharing conclusions through a limited memory interface, utilizing a deterministic benchmark guard, and accessing read-only cross-agent state. This setup allows for broader exploration of optimization possibilities within a fixed computational budget. The framework's design enables it to identify and implement highly efficient kernel configurations. Evaluations on NVIDIA H100 and B200 GPUs using representative workloads from SOL-ExecBench demonstrated KernelArc's effectiveness. It produced top-ranking implementations for various tasks, including custom GEMM operations, fused mixture-of-experts backward passes, and optimized attention mechanisms. The results highlight that a shared multi-agent search can significantly enhance optimization outcomes.

Why it matters

For professionals in high-performance computing and AI infrastructure, automated GPU kernel optimization can drastically improve efficiency, reduce development time, and lower operational costs.

How to implement this in your domain

  1. 1Explore multi-agent optimization frameworks for your specific GPU-intensive workloads.
  2. 2Benchmark current GPU kernel performance against state-of-the-art automated optimization results.
  3. 3Investigate integrating autonomous optimization tools into your CI/CD pipelines for performance tuning.
  4. 4Allocate resources for research and development into agent-based systems for infrastructure optimization.

Original post by Joyjit Kundu, Ben Stoffelen, Kaili Wang, Peter Vrancx, Ludovic Denoyer

"arXiv:2608.17071v1 Announce Type: new Abstract: We present KernelArc, a multi-agent framework for autonomous GPU kernel optimization across heterogeneous workloads. Strategy-specialized agents run in parallel and coordinate through conclusions-only shared memory, a deterministic…"

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Originally posted by Joyjit Kundu, Ben Stoffelen, Kaili Wang, Peter Vrancx, Ludovic Denoyer on X · view source

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