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Compile-Time Resource Safety for GPU APIs: A Low-Overhead Typestate Framework

Tihomir Thomas Bicanic

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Tags: Computer science, CUDA, nVidia, nVidia GeForce RTX 3090, OpenCL, Package, Performance, Rust, Security

October 5, 2025 by hgpu

exa-AMD: An Exascale-Ready Framework for Accelerating the Discovery and Design of Functional Materials

Weiyi Xiaa, Maxim Moraru, Ying Wai Li, Cai-Zhuang Wang

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Tags: Algorithms, Benchmarking, Condensed matter, Materials Science, Package, Physics

October 5, 2025 by hgpu

TRUST: the HPC open-source CFD platform – from CPU to GPU

Elie Saikali∗ , Adrien Bruneton, Pierre Ledac, Remi Bourgeois

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Tags: AMD Radeon Instinct MI250A, AMD Radeon Instinct MI300A, ATI, cfd, CUDA, Fluid dynamics, HIP, MPI, Numerical simulation, nVidia, nVidia A100, OpenMP, Package

September 28, 2025 by hgpu

Mojo: MLIR-Based Performance-Portable HPC Science Kernels on GPUs for the Python Ecosystem

William F. Godoy, Tatiana Melnichenko, Pedro Valero-Lara, Wael Elwasif, Philip Fackler, Rafael Ferreira Da Silva, Keita Teranishi, Jeffrey S. Vetter

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Tags: AI, AMD Radeon Instinct MI300A, ATI, Compilers, Computer science, CUDA, HIP, HPC, nVidia, nVidia H100, Package, Python, ROCm

September 28, 2025 by hgpu

Towards Robust Agentic CUDA Kernel Benchmarking, Verification, and Optimization

Robert Tjarko Lange, Qi Sun, Aaditya Prasad, Maxence Faldor, Yujin Tang, David Ha

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Tags: Benchmarking, Computer science, CUDA, Filtering, LLM, nVidia, nVidia H100, Package, Software Engineering

September 21, 2025 by hgpu

Dato: A Task-Based Programming Model for Dataflow Accelerators

Shihan Fang, Hongzheng Chen, Niansong Zhang, Jiajie Li, Han Meng, Adrian Liu, Zhiru Zhang

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Tags: Computer science, FPGA, nVidia, Package, Programming Languages, Python

September 21, 2025 by hgpu

An HPC Benchmark Survey and Taxonomy for Characterization

Andreas Herten, Olga Pearce, Filipe S. M. Guimarães

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Tags: Benchmarking, Computer science, CUDA, Fortran, HIP, HPC, MPI, OpenACC, OpenCL, OpenMP, Package, Performance, ROCm, SYCL

September 14, 2025 by hgpu

Home-made Diffusion Model from Scratch to Hatch

Shih-Ying Yeh

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Tags: Computer science, Computer vision, nVidia, nVidia GeForce RTX 5090, Package, Python, PyTorch

September 14, 2025 by hgpu

High Performance Matrix Multiplication

Ethan Davis

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Tags: BLAS, Computer science, CUBLAS, CUDA, Linear Algebra, Matrix multiplication, nVidia, OpenMP, Package, Performance, Python, Tesla V100

September 14, 2025 by hgpu

CrossTL: A Universal Programming Language Translator with Unified Intermediate Representation

Nripesh Niketan, Vaatsalya Shrivastva

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Tags: Compilers, Computer science, CUDA, DirectX, GLSL, HIP, HLSL, nVidia, OpenGL, Package, Programming Languages, Vulkan

September 7, 2025 by hgpu

Harnessing Batched BLAS/LAPACK Kernels on GPUs for Parallel Solutions of Block Tridiagonal Systems

David Jin, Alexis Montoison, Sungho Shin

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Tags: AMD Radeon Instinct MI300X, ATI, Benchmarking, BLAS, Computer science, CUDA, Factorization, Julia, nVidia, nVidia H200, Package, ROCm

September 7, 2025 by hgpu

GPU-acceleration of the Discontinuous Galerkin Shallow Water Equations Solver (DG-SWEM) using CUDA and OpenACC

Chayanon Wichitrnithed, Eirik Valseth, Clint Dawson

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Tags: cfd, Computational Physics, CUDA, Data parallelism, Fluid dynamics, Fortran, MPI, nVidia, nVidia GH200, OpenACC, Package, Physics

September 7, 2025 by hgpu

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

See all packages

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high performance computing on graphics processing units: hgpu.org

Compile-Time Resource Safety for GPU APIs: A Low-Overhead Typestate Framework

exa-AMD: An Exascale-Ready Framework for Accelerating the Discovery and Design of Functional Materials

TRUST: the HPC open-source CFD platform – from CPU to GPU

Mojo: MLIR-Based Performance-Portable HPC Science Kernels on GPUs for the Python Ecosystem

Towards Robust Agentic CUDA Kernel Benchmarking, Verification, and Optimization

Dato: A Task-Based Programming Model for Dataflow Accelerators

An HPC Benchmark Survey and Taxonomy for Characterization

Home-made Diffusion Model from Scratch to Hatch

High Performance Matrix Multiplication

CrossTL: A Universal Programming Language Translator with Unified Intermediate Representation

Harnessing Batched BLAS/LAPACK Kernels on GPUs for Parallel Solutions of Block Tridiagonal Systems

GPU-acceleration of the Discontinuous Galerkin Shallow Water Equations Solver (DG-SWEM) using CUDA and OpenACC

Recent source codes

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

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