GPUPhysBench: Benchmarking Coding Agents for Correct and Efficient GPU Physics Simulation
cs.DC, cs.AI
Submitted: 2026-09-28
Updated: 2026-09-28
Terminology
Sources
- K-Search: LLM Kernel Generation via Co-Evolving Intrinsic World Model
- AVO: Agentic Variation Operators for Autonomous Evolutionary Search
- AutoPDE: Reliable Agentic PDE Solving via Explicitly Represented Solver Strategies
- PDEAgent-Bench: A Multi-Metric, Multi-Library Benchmark for PDE Solver Generation
- TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators
- KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta
- Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning
- CFDLLMBench: A Benchmark Suite for Evaluating Large Language Models in Computational Fluid Dynamics
- KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization
- Geak: Introducing Triton Kernel AI Agent & Evaluation Benchmarks
- OpInf-LLM: Parametric PDE Solving with LLMs via Operator Inference
- Astra: A Multi-Agent System for GPU Kernel Performance Optimization
- MultiKernelBench: A Multi-Platform Benchmark for Kernel Generation
- MKEvolve: A Modular Multi-Agent Framework for Kernel Code Generation
- Foam-Agent 2.0: An End-to-End Composable Multi-Agent Framework for Automating CFD Simulation in OpenFOAM
- AccelOpt: A Self-Improving LLM Agentic System for AI Accelerator Kernel Optimization
- CUDABench: Benchmarking LLMs for Text-to-CUDA Generation
Related papers
- iScheduler: Reinforcement Learning-Driven Continual Optimization for Large-Scale Resource Investment Problems
- SAMM: Sharded Automated Market Maker
- InferScale: GPU-Native KV Injection for Personalized LLM Serving
- Vigil: Accountable Liveness against Selective Silence
- Steelhead: Interleaving Partially Synchronous and Asynchronous Commit Rules on a Shared DAG
- Democratizing MoE inference on commodity GPUs with CoMoE