high performance computing on graphics processing units: hgpu.org

Packages

hgpu.org » Applications

SOAP3-dp: Fast, Accurate and Sensitive GPU-based Short Read Aligner

Ruibang Luo, Thomas Wong, Jianqiao Zhu, Chi-Man Liu, Edward Wu, Lap-Kei Lee, Haoxiang Lin, Wenjuan Zhu, David W. Cheung, Hing-Fung Ting, Siu-Ming Yiu, Chang Yu, Yingrui Li, Ruiqiang Li, Tak-Wah Lam

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Tags: Algorithms, Bioinformatics, Biology, CUDA, Next-Generation sequencing, nVidia, Package, Tesla C2070, Tesla M2050

February 25, 2013 by hgpu

Graphics Card as a Cheap Supercomputer

Jan Prikryl

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 275, Overview, Package

February 25, 2013 by hgpu

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

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

MSKernelBench & CUDAMaster

Making LLMs Optimize Multi-Scenario CUDA Kernels Like Experts

EvoScientist: Harness Vibe Research with Self-evolving AI Scientists

EvoScientist: Towards Multi-Agent Evolving AI Scientists for End-to-End Scientific Discovery

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build & Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build & Test Pipeline of Code Repositories on ANY Language and ANY Platform

CONCUR: a benchmark designed to evaluate multithreaded Java code generated by LLMs

CONCUR: Benchmarking LLMs for Concurrent Code Generation

HIPRT: Ray Tracing using HIP

Ray Tracing using HIP

MXFP4 Training Support Codebase

Practical FP4 Training for Large-Scale MoE Models on Hopper GPUs

CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation

CUDABench: Benchmarking LLMs for Text-to-CUDA Generation

See all packages

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high performance computing on graphics processing units: hgpu.org

Packages

SOAP3-dp: Fast, Accurate and Sensitive GPU-based Short Read Aligner

Graphics Card as a Cheap Supercomputer

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

Recent source codes

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

MSKernelBench & CUDAMaster

EvoScientist: Harness Vibe Research with Self-evolving AI Scientists

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

CONCUR: a benchmark designed to evaluate multithreaded Java code generated by LLMs

HIPRT: Ray Tracing using HIP

MXFP4 Training Support Codebase

CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation

CUDABench: Benchmarking LLMs for Text-to-CUDA Generation

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