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Kerncap: Automated Kernel Extraction and Isolation for AMD GPUs

Cole Ramos, Keith Lowery

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Tags: AMD, AMD Radeon Instinct MI210, AMD Radeon Instinct MI300X, AMD Radeon Pro W7900, Computer science, LLM, Package, Python, ROCm, Triton

May 11, 2026 by hgpu

KEET: Explaining Performance of GPU Kernels Using LLM Agents

Joshua H. Davis, Klaudiusz Rydzy, Srinivasan Ramesh, Aadit Nilay, Daniel Nichols, Swapna Raj, Nikhil Jain, Abhinav Bhatele

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Tags: Computer science, CUDA, LLM, nVidia, nVidia H100, Performance

May 11, 2026 by hgpu

CuBridge: An LLM-Based Framework for Understanding and Reconstructing High-Performance Attention Kernels

Xing Ma, Yangjie Zhou, Wu Sun, Zihan Liu, Jingwen Leng, Yun Lin, Shixuan Sun, Minyi Guo, Jin Song Dong

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Tags: Computer science, CUDA, Deep learning, LLM, nVidia, nVidia A100, nVidia H100

May 11, 2026 by hgpu

Microbenchmark-Driven Analytical Performance Modeling Across Modern GPU Architectures

Aaron Jarmusch, Sunita Chandrasekaran

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Tags: AMD, AMD Radeon Instinct MI250X, AMD Radeon Instinct MI300A, Benchmarking, Computer science, CUDA, nVidia, nVidia B200

May 11, 2026 by hgpu

ARGUS: Agentic GPU Optimization Guided by Data-Flow Invariants

Haohui Mai, Xiaoyan Guo, Xiangyun Ding, Daifeng Li, Qiuchu Yu, Chenzhun Guo, Cong Wang, Jiacheng Zhao, Christos Kozyrakis, Binhang Yuan

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Tags: AMD, AMD Radeon Instinct MI300X, Computer science, DSL, LLM, Triton

May 3, 2026 by hgpu

FACT: Compositional Kernel Synthesis with a Three-Stage Agentic Workflow

Sina Heidari, Dimitrios S. Nikolopoulos

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Tags: Computer science, CUBLAS, CUDA, Deep learning, nVidia, nVidia A100

May 3, 2026 by hgpu

Revealing NVIDIA Closed-Source Driver Command Streams for CPU-GPU Runtime Behavior Insight

Yuang Yan, Ian Karlin, Ryan Grant

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Tags: Computer science, CUDA, nVidia, nVidia A40, Performance

May 3, 2026 by hgpu

A Human–Machine Collaborative Tuning Framework for Triton Kernel Optimization on SIMD Platforms

Xulin Zhou, Hongbin Zhang, Mingjie Xing

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Tags: Auto-Tuning, Computer science, Evolutionary Computations, Triton

May 3, 2026 by hgpu

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

Divakar Kumar Yadav, Tian Zhao, Deepak Kumar

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Tags: Computer science, CUBLAS, CUDA, LLM, nVidia, nVidia B200, nVidia H100, nVidia RTX PRO 6000, Package, Performance, Triton

May 3, 2026 by hgpu

DVM: Real-Time Kernel Generation for Dynamic AI Models

Jingzhi Fang, Xiong Gao, Renwei Zhang, Zichun Ye, Lei Chen, Jie Zhao, Chengnuo Huang, Hui Xu, Xuefeng Jin

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Tags: Code generation, Computer science, LLM, Package

April 13, 2026 by hgpu

Agentic Code Optimization via Compiler-LLM Cooperation

Benjamin Mikek, Danylo Vashchilenko, Bryan Lu, Panpan Xu

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Tags: Code generation, Computer science, LLM, Package

April 13, 2026 by hgpu

CuTeGen: An LLM-Based Agentic Framework for Generation and Optimization of High-Performance GPU Kernels using CuTe

Tara Saba, Anne Ouyang, Xujie Si, Fan Long

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Tags: Code generation, Computer science, CUDA, Machine learning, Matrix multiplication, nVidia, nVidia GeForce RTX 4090

April 13, 2026 by hgpu

Harness Engineering for LLM-Driven GPU Kernel Generation

CANNBot Skills

Compiler-Grounded Hierarchical Diagnosis for LLM-Based Triton Kernel Optimization

NVIDIA Object Oriented Agents

NVIDIA-labs OO Agents: Native Python Object-Oriented Agents

UniCoder: Unified Visual-to-Code Generation via Symbolic Rewards and Reference-Guided Code Optimization

CuFuzz: An API-Knowledge-Graph Coverage-Driven Fuzzing Framework for CUDA Libraries

AutoPass: Evidence-Guided LLM Agents for Compiler Performance Tuning

Probe-and-Refine Tuning of Repository Guidance for AI Coding Agents

Probe-and-Refine Tuning of Repository Guidance for Coding Agents

See all packages

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high performance computing on graphics processing units: hgpu.org

Kerncap: Automated Kernel Extraction and Isolation for AMD GPUs

KEET: Explaining Performance of GPU Kernels Using LLM Agents

CuBridge: An LLM-Based Framework for Understanding and Reconstructing High-Performance Attention Kernels

Microbenchmark-Driven Analytical Performance Modeling Across Modern GPU Architectures

ARGUS: Agentic GPU Optimization Guided by Data-Flow Invariants

FACT: Compositional Kernel Synthesis with a Three-Stage Agentic Workflow

Revealing NVIDIA Closed-Source Driver Command Streams for CPU-GPU Runtime Behavior Insight

A Human–Machine Collaborative Tuning Framework for Triton Kernel Optimization on SIMD Platforms

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

DVM: Real-Time Kernel Generation for Dynamic AI Models

Agentic Code Optimization via Compiler-LLM Cooperation

CuTeGen: An LLM-Based Agentic Framework for Generation and Optimization of High-Performance GPU Kernels using CuTe

Recent source codes

TensorCorePTX: PTX Tensor Core GEMM Exploration

FlashPDE: Triton-fused PDE stencil kernels for physics-informed neural networks

PortLBM: Portable GPU-accelerated Lattice Boltzmann Method Simulations

Harness Engineering for LLM-Driven GPU Kernel Generation

CANNBot Skills

NVIDIA Object Oriented Agents

UniCoder: Unified Visual-to-Code Generation via Symbolic Rewards and Reference-Guided Code Optimization

CuFuzz: An API-Knowledge-Graph Coverage-Driven Fuzzing Framework for CUDA Libraries

AutoPass: Evidence-Guided LLM Agents for Compiler Performance Tuning

Probe-and-Refine Tuning of Repository Guidance for AI Coding Agents

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