high performance computing on graphics processing units: hgpu.org

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Mixed-precision numerics in scientific applications: survey and perspectives

Aditya Kashi, Hao Lu, Wesley Brewer, David Rogers, Michael Matheson, Mallikarjun Shankar, Feiyi Wang

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Tags: AI, Computer science, Mixed precision, nVidia, nVidia V100, Review

March 26, 2026 by hgpu

Scaling GPU-to-CPU Migration for Efficient Distributed Execution on CPU Clusters

Ruobing Han, Hyesoon Kim

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Tags: Compilers, Computer science, CUDA, nVidia, nVidia A100, nVidia V100, Triton

February 8, 2026 by hgpu

Accurate Models of NVIDIA Tensor Cores

Faizan A. Khattak, Mantas Mikaitis

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Tags: Computer science, CUDA, Matrix multiplication, nVidia, nVidia B200, nVidia H100, nVidia V100, Package

December 15, 2025 by hgpu

MT4G: A Tool for Reliable Auto-Discovery of NVIDIA and AMD GPU Compute and Memory Topologies

Stepan Vanecek, Manuel Walter Mussbacher, Dominik Groessler, Urvij Saroliya, Martin Schulz

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Tags: AMD Radeon Instinct MI100, AMD Radeon Instinct MI210, AMD Radeon Instinct MI300X, ATI, Benchmarking, Computer science, CUDA, HIP, nVidia, nVidia A100, nVidia GeForce RTX 2080, nVidia H100, nVidia Quadro P 6000, nVidia V100, Package, PTX

November 16, 2025 by hgpu

Tutoring LLM into a Better CUDA Optimizer

Matyáš Brabec, Jiří Klepl, Michal Töpfer, Martin Kruliš

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Tags: Code generation, Computer science, CUDA, LLM, nVidia, nVidia A100, nVidia H100, nVidia V100, Package

October 26, 2025 by hgpu

BePilot: An AI Programming Assistant for Compiler Backend Development

Ming Zhong, Xin Sun, Fang Lv, Lulin Wang, Hongna Geng, Lei Qiu, Huimin Cui, Xiaobing Feng

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Tags: AI, Code generation, Compilers, Computer science, LLM, nVidia, nVidia A100, nVidia V100

August 31, 2025 by hgpu

SIGMo: High-Throughput Batched Subgraph Isomorphism on GPUs for Molecular Matching

Antonio De Caro, Gennaro Cordasco, Federico Ficarelli, Biagio Cosenza

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Tags: Biology, Chemistry, Computer science, CUDA, MPI, nVidia, nVidia V100, oneAPI, Package

August 10, 2025 by hgpu

Specx: a C++ task-based runtime system for heterogeneous distributed architectures

Paul Cardosi, Bérenger Bramas

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Tags: Computer science, CUDA, Heterogeneous systems, HIP, nVidia, nVidia A100, nVidia P100, nVidia Quadro RTX 8000, nVidia V100, Package, Task scheduling

July 20, 2025 by hgpu

LiteGD: Lightweight and dynamic GPU Dispatching for Large-scale Heterogeneous Clusters

Kunming Zhang, Hanlong Liao, Guoming Tang

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Tags: Computer science, GPU cluster, Heterogeneous systems, Machine learning, nVidia, nVidia A800, nVidia GeForce RTX 4090, nVidia H100, nVidia RTX A6000, nVidia V100

June 22, 2025 by hgpu

Acceleration as a Service (XaaS) Source Containers

Eiman Alnuaimi

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Tags: Computer science, Heterogeneous systems, HPC, Intel, Intel Data Center GPU Max 1550, LLM, MPI, nVidia, nVidia GH200, nVidia V100, Optimization, Package, performance portability, Thesis

June 8, 2025 by hgpu

A Microbenchmark Framework for Performance Evaluation of OpenMP Target Offloading

Mohammad Atif, Tianle Wang, Zhihua Dong, Charles Leggett, Meifeng Lin

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Tags: Benchmarking, Computer science, Heterogeneous systems, nVidia A100, nVidia RTX A6000, nVidia V100, OpenMP, Package

March 10, 2025 by hgpu

On the Partitioning of GPU Power among Multi-Instances

Tirth Vamja, Kaustabha Ray, Felix George, UmaMaheswari C Devi

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Tags: Computer science, CUDA, Energy-efficient computing, Machine learning, Matrix multiplication, nVidia, nVidia A100, nVidia V100, Performance

February 3, 2025 by hgpu

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

Device Virtual Machine (DVM)

DVM: Real-Time Kernel Generation for Dynamic AI Models

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

AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search

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

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

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

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

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high performance computing on graphics processing units: hgpu.org

Mixed-precision numerics in scientific applications: survey and perspectives

Scaling GPU-to-CPU Migration for Efficient Distributed Execution on CPU Clusters

Accurate Models of NVIDIA Tensor Cores

MT4G: A Tool for Reliable Auto-Discovery of NVIDIA and AMD GPU Compute and Memory Topologies

Tutoring LLM into a Better CUDA Optimizer

BePilot: An AI Programming Assistant for Compiler Backend Development

SIGMo: High-Throughput Batched Subgraph Isomorphism on GPUs for Molecular Matching

Specx: a C++ task-based runtime system for heterogeneous distributed architectures

LiteGD: Lightweight and dynamic GPU Dispatching for Large-scale Heterogeneous Clusters

Acceleration as a Service (XaaS) Source Containers

A Microbenchmark Framework for Performance Evaluation of OpenMP Target Offloading

On the Partitioning of GPU Power among Multi-Instances

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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