high performance computing on graphics processing units: hgpu.org

Yang Yu, Peiyu Zang, Chi Hsu Tsai, Haiming Wu, Yixin Shen, Jialing Zhang, Haoyu Wang, Zhiyou Xiao, Jingze Shi, Yuyu Luo, Wentao Zhang, Chunlei Men, Guang Liu, Yonghua Lin

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Tags: AMD, Benchmarking, Computer science, CUDA, LLM, nVidia, ROCm, Triton

January 25, 2026 by hgpu

PEAK: A Performance Engineering AI-Assistant for GPU Kernels Powered by Natural Language Transformations

Muhammad Usman Tariq, Abhinav Jangda, Angelica Moreira, Madan Musuvathi, Tyler Sorensen

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Tags: AI, AMD, AMD Radeon Instinct MI200, ATI, Computer science, CUDA, HIP, HLSL, LLM, NLP, nVidia, nVidia RTX A6000

December 29, 2025 by hgpu

Chopper: A Multi-Level GPU Characterization Tool & Derived Insights Into LLM Training Inefficiency

Marco Kurzynski, Shaizeen Aga, Di Wu

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Tags: AMD, AMD Radeon Instinct MI300X, ATI, Benchmarking, Computer science, Data parallelism, LLM, ROCm

December 15, 2025 by hgpu

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

Ryan Swann, Muhammad Osama, Xiaohu Guo, Bryant Nelson, Lixun Zhang, Alex Brown, Yen Ong, Ali Yazdani, Sean Siddens, Ganesh Dasika, Alex Underwood

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Source codes

Tags: AMD, AMD Radeon Instinct MI300X, AMD Radeon Instinct MI350X, ATI, BLAS, Computer science, HPC, Package, Performance, ROCm, Triton

December 7, 2025 by hgpu

A Compute Graph Simulation and Implementation Framework Targeting AMD Versal AI Engines

Jonathan Strobl, Leonardo Solis-Vasquez, Yannick Lavan, Andreas Koch

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Tags: AI, AMD, Code generation, Computer science, FPGA

October 26, 2025 by hgpu

high performance computing on graphics processing units: hgpu.org

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

An Efficient Heterogeneous Co-Design for Fine-Tuning on a Single GPU

Diagnosing FP4 inference: a layer-wise and block-wise sensitivity analysis of NVFP4 and MXFP4

Ray Tracing using HIP

Joint Training on AMD and NVIDIA GPUs

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

HetCCL: Accelerating LLM Training with Heterogeneous GPUs

Towards Automated Kernel Generation in the Era of LLMs

PEAK: A Performance Engineering AI-Assistant for GPU Kernels Powered by Natural Language Transformations

Chopper: A Multi-Level GPU Characterization Tool & Derived Insights Into LLM Training Inefficiency

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

A Compute Graph Simulation and Implementation Framework Targeting AMD Versal AI Engines

Recent source codes

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

Agentic Code Optimization via Compiler-LLM Cooperation

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

True 4-Bit Quantized CNN Training on CPU

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

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