high performance computing on graphics processing units: hgpu.org

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Enhancing the Performance Analysis of NCCL GPU Collectives

Jurij Cerar

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Tags: Computer science, CUDA, nVidia, nVidia H100, Performance, Tesla T4, Thesis

July 13, 2026 by hgpu

Fearless Concurrency on the GPU

Melih Elibol, Jared Roesch, Isaac Gelado, Eric Buehler, Michael Garland

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Tags: Computer science, CUBLAS, CUDA, nVidia, nVidia B200, nVidia GeForce RTX 5090, Performance, Python, Rust

June 17, 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

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

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

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

Qitong Sun, Jun Han, Tianlin Li, Zhe Tang, Sheng Chen, Fei Yang, Aishan Liu, Xianglong Liu, Yang Liu

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Tags: Computer science, CUDA, LLM, nVidia, nVidia A100, Package, Performance, PyTorch

March 15, 2026 by hgpu

KernelBlaster: Continual Cross-Task CUDA Optimization via Memory-Augmented In-Context Reinforcement Learning

Kris Shengjun Dong, Sahil Modi, Dima Nikiforov, Sana Damani, Edward Lin, Siva Kumar Sastry Hari, Christos Kozyrakis

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Tags: Computer science, CUDA, LLM, nVidia, nVidia A100, nVidia H100, nVidia L40s, nVidia RTX A6000, Performance

February 23, 2026 by hgpu

OptiML: An End-to-End Framework for Program Synthesis and CUDA Kernel Optimization

Arijit Bhattacharjee, Heng Ping, Son Vu Le, Paul Bogdan, Nesreen K. Ahmed, Ali Jannesari

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Tags: Computer science, CUDA, LLM, nVidia, nVidia A100, Performance

February 23, 2026 by hgpu

Execution-Centric Characterization of FP8 Matrix Cores, Asynchronous Execution, and Structured Sparsity on AMD MI300A

Aaron Jarmusch, Connor Vitz, Sunita Chandrasekaran

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Tags: AMD, AMD Radeon Instinct MI300A, Benchmarking, Computer science, HIP, Performance, ROCm

February 16, 2026 by hgpu

Improving HPC Code Generation Capability of LLMs via Online Reinforcement Learning with Real-Machine Benchmark Rewards

Ryo Mikasa, Shun-ichiro Hayashi, Daichi Mukunoki, Tetsuya Hoshino, Takahiro Katagiri

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Tags: Benchmarking, Code generation, Computer science, CUDA, HPC, LLM, Matrix multiplication, nVidia, nVidia H100, OpenMP, Performance

February 16, 2026 by hgpu

Deep Kernel Fusion for Transformers

Zixi Zhang, Zhiwen Mo, Yiren Zhao, Robert Mullins

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Tags: Computer science, CUDA, LLM, nVidia, nVidia A100, Performance

February 16, 2026 by hgpu

Nsight Python: A Python-First Profiling Toolkit for Seamless GPU Kernel Analysis (Tool)

Bastian Hagedorn, Alexander Collins, Tony Mongkolsmai, Vinod Grover

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Tags: Computer science, CUDA, nVidia, nVidia B200, Package, Performance, Profiling, Python, Triton

February 2, 2026 by hgpu

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

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CUDA Kernel Fusion Benchmarks

Analyzing the Impact of Kernel Fusion on GPU Tensor Operation Performance: A Systematic Performance Study

IntelliKit: Agent-first tooling for AMD hardware

Kerncap: Automated Kernel Extraction and Isolation for AMD GPUs

DITRON: Distributed Compiler based on Triton for Parallel Systems

DITRON: Distributed Multi-level Tiling Compiler for Parallel Tensor Programs

See all packages

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high performance computing on graphics processing units: hgpu.org

Enhancing the Performance Analysis of NCCL GPU Collectives

Fearless Concurrency on the GPU

KEET: Explaining Performance of GPU Kernels Using LLM Agents

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

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

KernelBlaster: Continual Cross-Task CUDA Optimization via Memory-Augmented In-Context Reinforcement Learning

OptiML: An End-to-End Framework for Program Synthesis and CUDA Kernel Optimization

Execution-Centric Characterization of FP8 Matrix Cores, Asynchronous Execution, and Structured Sparsity on AMD MI300A

Improving HPC Code Generation Capability of LLMs via Online Reinforcement Learning with Real-Machine Benchmark Rewards

Deep Kernel Fusion for Transformers

Nsight Python: A Python-First Profiling Toolkit for Seamless GPU Kernel Analysis (Tool)

Recent source codes

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

CUDAnalyst (CUDA + Analyst)

CodegenBench

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CUDA Kernel Fusion Benchmarks

IntelliKit: Agent-first tooling for AMD hardware

DITRON: Distributed Compiler based on Triton for Parallel Systems

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