high performance computing on graphics processing units: hgpu.org

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PRAND: GPU accelerated parallel random number generation library: Using most reliable algorithms and applying parallelism of modern GPUs and CPUs

L.Yu. Barash, L.N. Shchur

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Tags: Algorithms, Computational Physics, Computer science, CUDA, Mathematical Software, nVidia, Package, Pseudo-random number generators, Tesla C2050

July 25, 2013 by hgpu

The Astrophysical Multipurpose Software Environment

F.I. Pelupessy, A. van Elteren, N. de Vries, S.L.W. McMillan, N. Drost, S.F. Portegies Zwart

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Tags: Astrophysics, CUDA, Gravitation, Instrumentation and Methods for Astrophysics, MPI, nVidia, Package, Python

July 21, 2013 by hgpu

Image reconstruction in digital holographic microscopy on GPU

Andrej Krejcir

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Tags: Algorithms, CUDA, Image processing, Image reconstruction, Microscopy, nVidia, nVidia GeForce GTX 470, Optimization, Package, Thesis

July 21, 2013 by hgpu

GPU Computing in Economics

Eric M. Aldrich

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Tags: CUDA, Finance, nVidia, Package, Review, Tesla C2075

July 20, 2013 by hgpu

Early Experiences With The OpenMP Accelerator Model

Chunhua Liao, Yonghong Yan, Bronis R. de Supinski, Daniel J. Quinlan, Barbara Chapman

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenACC, Package, Tesla K20

July 17, 2013 by hgpu

KernelGen – the design and implementation of a next generation compiler platform for accelerating numerical models on GPUs

Dmitry Mikushin, Nikolay Likhogrud, Eddy Zheng Zhang, Christopher Bergstrom

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Tags: Code generation, Compilers, Computer science, CUDA, Fortran, nVidia, OpenACC, Package, PTX, Tesla K20

July 15, 2013 by hgpu

The CUDA Handbook: A Comprehensive Guide to GPU Programming

Nicholas Wilt

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Tags: Book, Computer science, CUDA, Image processing, N-body simulation, nVidia, Overview, Package

July 13, 2013 by hgpu

Parallel Graph Processing on Graphics Processors Made Easy

Jianlong Zhong, Bingsheng He

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Tags: Algorithms, Computer science, CUDA, Graph theory, nVidia, Package, Tesla C2050

July 12, 2013 by hgpu

ParadisEO-MO-GPU: a Framework for Parallel GPU-based Local Search Metaheuristics

Nouredine Melab, The van Luong, Karima Boufaras, El-Ghazali Talbi

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Tags: Computer science, CUDA, Metaheuristics, nVidia, nVidia GeForce GTX 480, Package, Performance, Search

July 8, 2013 by hgpu

CrowdCL: Web-Based Volunteer Computing with WebCL

Tommy MacWilliam, Cris Cecka

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Tags: Computer science, Distributed computing, Java, nVidia, nVidia GeForce GT 320 M, OpenCL, Package, Tesla K20

July 7, 2013 by hgpu

Computing Strongly Connected Components with CUDA

Miroslav Stuhl

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Package, Thesis

July 3, 2013 by hgpu

vSMC: Parallel Sequential Monte Carlo in C++

Yan Zhou

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Tags: Algorithms, Bayesian, Mathematical Software, Mathematics, nVidia, nVidia Quadro FX 2000, OpenCL, Package, Physics, Signal processing, Statistics

July 1, 2013 by hgpu

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

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

Agentic Code Optimization via Compiler-LLM Cooperation

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

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

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high performance computing on graphics processing units: hgpu.org

Applications

PRAND: GPU accelerated parallel random number generation library: Using most reliable algorithms and applying parallelism of modern GPUs and CPUs

The Astrophysical Multipurpose Software Environment

Image reconstruction in digital holographic microscopy on GPU

GPU Computing in Economics

Early Experiences With The OpenMP Accelerator Model

KernelGen – the design and implementation of a next generation compiler platform for accelerating numerical models on GPUs

The CUDA Handbook: A Comprehensive Guide to GPU Programming

Parallel Graph Processing on Graphics Processors Made Easy

ParadisEO-MO-GPU: a Framework for Parallel GPU-based Local Search Metaheuristics

CrowdCL: Web-Based Volunteer Computing with WebCL

Computing Strongly Connected Components with CUDA

vSMC: Parallel Sequential Monte Carlo in C++

Recent source codes

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

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Agentic Code Optimization via Compiler-LLM Cooperation

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

Device Virtual Machine (DVM)

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

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