high performance computing on graphics processing units: hgpu.org

Packages

hgpu.org » Applications

RenderAnts: Interactive REYES Rendering on GPUs

Kun Zhou, Qiming Hou, Zhong Ren, Minmin Gong, Xin Sun, Baining Guo

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Tags: Computer science, Image generation, nVidia, nVidia GeForce GTX 280, Package, Raytracing, Rendering, Shaders

November 5, 2010 by hgpu

The Scalable Heterogeneous Computing (SHOC) benchmark suite

Anthony Danalis,Gabriel Marin,Collin McCurdy,Jeremy S. Meredith,Philip C. Roth,Kyle Spafford,Vinod Tipparaju,Jeffrey S. Vetter

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Tags: ATI, ATI Radeon HD 5770, ATI Radeon HD 5870, ATI Stream, Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce 8800 GT, nVidia GeForce 8800 GTS, nVidia GeForce 8800 GTX, nVidia GeForce 9400 M, OpenCL, Package, Performance, Tesla C1060

November 5, 2010 by hgpu

Lattice SU(2) on GPU’s

Nuno Cardoso, Pedro Bicudo

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Tags: Computational Physics, CUDA, High Energy Physics - Lattice, Monte Carlo simulation, nVidia, nVidia GeForce GTX 295, Package, Physics

November 5, 2010 by hgpu

Solving Path Problems on the GPU

Aydin Buluc, John R. Gilbert, Ceren Budak

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Tags: Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce 8800 Ultra, Package, Path problems

November 5, 2010 by hgpu

A Performance Comparison of CUDA and OpenCL

Kamran Karimi, Neil G. Dickson, Firas Hamze

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Tags: Computational Physics, Computer science, CUDA, nVidia, nVidia GeForce GTX 260, OpenCL, Package, Performance

November 5, 2010 by hgpu

Treecode and fast multipole method for N-body simulation with CUDA

Rio Yokota, Lorena Barba

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Tags: Computational Physics, CUDA, N-body simulation, nVidia, nVidia GeForce GTX 295, Package, Physics

November 4, 2010 by hgpu

CUDASW++: optimizing Smith-Waterman sequence database searches for CUDA-enabled graphics processing units

Yongchao Liu, Douglas Maskell, Bertil Schmidt

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Tags: Bioinformatics, Biology, Computational biology, CUDA, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 295, Package, Smith-Waterman algorithm

November 4, 2010 by hgpu

Feature tracking and matching in video using programmable graphics hardware

Sudipta Sinha, Jan-Michael Frahm, Marc Pollefeys, Yakup Genc

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Tags: Computer science, Computer vision, nVidia, nVidia GeForce 7800 GTX, nVidia GeForce 7900 GTX, OpenGL, Package, SIFT, Video tracking

November 4, 2010 by hgpu

GPUCV: A Framework for Image Processing Acceleration with Graphics Processors

Jean-philippe Farrugia, Patrick Horain, Erwan Guehenneux, Yannick Alusse

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Tags: Computer science, Computer vision, GLSL, Image processing, nVidia, nVidia GeForce 7800 GTX, OpenGL, Package

November 4, 2010 by hgpu

Implementing sparse matrix-vector multiplication on throughput-oriented processors

Nathan Bell, Michael Garland

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Tags: Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 285, Package, Sparse matrix

November 4, 2010 by hgpu

CuPP – A framework for easy CUDA integration

Jens Breitbart

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Tags: Computer science, CUDA, High-level Languages, nVidia, Package

November 4, 2010 by hgpu

NBSymple, a double parallel, symplectic N-body code running on Graphic Processing Units

R. Capuzzo-Dolcetta, A. Mastrobuono-Battisti, D. Maschietti

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Tags: Astrophysics, CUDA, Gravitation, Instrumentation and Methods for Astrophysics, N-body simulation, nVidia, OpenMP, Package, Stellar dynamics, Tesla C1060

November 4, 2010 by hgpu

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

Experiences with implementing Kokkos’ SYCL backend

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

See all packages

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high performance computing on graphics processing units: hgpu.org

Packages

RenderAnts: Interactive REYES Rendering on GPUs

The Scalable Heterogeneous Computing (SHOC) benchmark suite

Lattice SU(2) on GPU’s

Solving Path Problems on the GPU

A Performance Comparison of CUDA and OpenCL

Treecode and fast multipole method for N-body simulation with CUDA

CUDASW++: optimizing Smith-Waterman sequence database searches for CUDA-enabled graphics processing units

Feature tracking and matching in video using programmable graphics hardware

GPUCV: A Framework for Image Processing Acceleration with Graphics Processors

Implementing sparse matrix-vector multiplication on throughput-oriented processors

CuPP – A framework for easy CUDA integration

NBSymple, a double parallel, symplectic N-body code running on Graphic Processing Units

Recent source codes

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

ROCm's implementation of Gromacs

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

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