high performance computing on graphics processing units: hgpu.org

Applications

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Scheduling a Parallel Sparse Direct Solver to Multiple GPUs

Kyungjoo Kim

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Tags: Computer science, CUDA, Factorization, FEM, Finite element method, Heterogeneous systems, nVidia, Package, Sparse direct solvers, Task scheduling, Tesla M2070

February 21, 2013 by hgpu

Chrono: a parallel multi-physics library for rigid-body, flexible-body, and fluid dynamics

H. Mazhar, T. Heyn, A. Pazouki, D. Melanz, A. Seidl, A. Bartholomew, A. Tasora, D. Negrut

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Tags: CUDA, Fluid dynamics, nVidia, Package, Physics, Visualization

February 21, 2013 by hgpu

ClusCo: clustering and comparison of protein models

Jamroz Michal, Kolinski Andrzej

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Tags: Biology, Biomolecules, Clustering, Computational Engineering, CUDA, Finance, nVidia, nVidia GeForce GTX 470, Package

February 20, 2013 by hgpu

Massively Parallel Computing in Economics

Eric M. Aldrich

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

February 15, 2013 by hgpu

Developing Performance-Portable Molecular Dynamics Kernels in OpenCL

S. J. Pennycook, S. A. Jarvis

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Tags: ATI, ATI FirePro V7800, ATI Radeon HD 6550, Benchmarking, Computer science, Molecular dynamics, nVidia, nVidia GeForce GTX 680, OpenCL, Package, Performance, Tesla C1060, Tesla C2050

February 14, 2013 by hgpu

A multi-lane traffic simulation model via continuous cellular automata

Emanuele Rodaro, Oznur Yeldan

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Tags: Cellular automata, Computer science, CUDA, nVidia, nVidia GeForce GT 555 M, Package, Python

February 9, 2013 by hgpu

Betweenness Centrality on GPUs and Heterogeneous Architectures

Ahmet Erdem Sariyuce, Kamer Kaya, Erik Saule, Umit V. Catalyurek

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Tags: Computer science, CUDA, Graph theory, Heterogeneous systems, nVidia, Package, Tesla C2050, Virtualization

February 7, 2013 by hgpu

Binary Interval Search: a scalable algorithm for counting interval intersections

Ryan M. Layer, Kevin Skadron, Gabriel Robins, Ira M. Hall, Aaron R. Quinlan

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Tags: Algorithms, Bioinformatics, Biology, Computer science, CUDA, Monte Carlo simulation, nVidia, Package, Sequence alignment

February 6, 2013 by hgpu

A Comparison of CPU and OpenCL Parallelization Methods for Correlation and Graph Layout Algorithms used in the Network Analysis of High Dimensional Data

Athanasios Theocharidis, Gibran Hemani, Michael Kargas, Tom C. Freeman

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Tags: ATI, ATI Radeon HD 5870, ATI Radeon HD 6970, Biology, Graph layout, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 580, OpenCL, OpenGL, Package, Visualization

February 5, 2013 by hgpu

Fast and Maliciously Secure Two-Party Computation Using the GPU

Tore Kasper Frederiksen, Jesper Buus Nielsen

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 670, Package, Security

February 3, 2013 by hgpu

XKaapi: A Runtime System for Data-Flow Task Programming on Heterogeneous Architectures

Thierry Gautier, Joao V. F. Lima, Nicolas Maillard, Bruno Raffin

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Tags: Computer science, CUDA, Factorization, Heterogeneous systems, Linear Algebra, nVidia, Package, Tesla C2050

February 2, 2013 by hgpu

Embedding OpenCL in GHC Haskell

Benedict R. Gaster, J. Garrett Morris

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Tags: ATI, Computer science, Haskell, OpenCL, Package

February 1, 2013 by hgpu

Kernel Library for LLM Serving

Compiler and Runtime Systems for Generative AI Models

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Genten: Software for Generalized Tensor Decompositions by Sandia National Laboratories

A Performance Portable Matrix Free Dense MTTKRP in GenTen

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Pinocchio: PINpointing Orbit Crossing Collapsed Hierarchical Objects

Accelerating cosmological simulations on GPUs: a portable approach using OpenMP

KernelCoder: trained on a curated dataset of reasoning traces and CUDA kernel pairs

ConCuR: Conciseness Makes State-of-the-Art Kernel Generation

VibeCodeHPC - Multi Agentic Vibe Coding for HPC

VibeCodeHPC: An Agent-Based Iterative Prompting Auto-Tuner for HPC Code Generation Using LLMs

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

exa-AMD: Exascale Accelerated Materials Discovery

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

See all packages

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high performance computing on graphics processing units: hgpu.org

Applications

Scheduling a Parallel Sparse Direct Solver to Multiple GPUs

Chrono: a parallel multi-physics library for rigid-body, flexible-body, and fluid dynamics

ClusCo: clustering and comparison of protein models

Massively Parallel Computing in Economics

Developing Performance-Portable Molecular Dynamics Kernels in OpenCL

A multi-lane traffic simulation model via continuous cellular automata

Betweenness Centrality on GPUs and Heterogeneous Architectures

Binary Interval Search: a scalable algorithm for counting interval intersections

A Comparison of CPU and OpenCL Parallelization Methods for Correlation and Graph Layout Algorithms used in the Network Analysis of High Dimensional Data

Fast and Maliciously Secure Two-Party Computation Using the GPU

XKaapi: A Runtime System for Data-Flow Task Programming on Heterogeneous Architectures

Embedding OpenCL in GHC Haskell

Recent source codes

Kernel Library for LLM Serving

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Genten: Software for Generalized Tensor Decompositions by Sandia National Laboratories

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Pinocchio: PINpointing Orbit Crossing Collapsed Hierarchical Objects

KernelCoder: trained on a curated dataset of reasoning traces and CUDA kernel pairs

VibeCodeHPC - Multi Agentic Vibe Coding for HPC

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

exa-AMD: Exascale Accelerated Materials Discovery

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