high performance computing on graphics processing units: hgpu.org

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hgpu.org » Applications

Matrix inversion speed up with CUDA

Jorge Soriano Pinedo

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Tags: Algorithms, Computer science, CUDA, Image reconstruction, Matrix inversion, Matrix multiplication, nVidia, Tesla C1060, Thesis

October 28, 2011 by hgpu

Parallel Random Numbers: As Easy as 1, 2, 3

John K. Salmon, Mark A. Moraes, Ron O. Dror, and David E. Shaw

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Tags: AES, ATI, ATI Radeon HD 6970, Computer science, CUDA, nVidia, nVidia GeForce GTX 580, OpenCL, Package, Performance, Pseudo-random number generators, Security

October 28, 2011 by hgpu

Programming Massively Parallel Processors with CUDA (audio course)

David B. Kirk, Wen-Mei W. Hwu

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Tags: Book, Computer science, CUDA, nVidia, OpenCL, Package, Programming techniques, Review, Tutorial

October 28, 2011 by hgpu

Implementing Domain-Specific Languages for Heterogeneous Parallel Computing

HyoukJoong Lee, Kevin J. Brown, Arvind K. Sujeeth, Hassan Chafi, Kunle Olukotun, Tiark Rompf, Martin Odersky

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Tags: Code generation, Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, Optimization, Tesla C2050

October 28, 2011 by hgpu

Dax: Data Analysis at Extreme

Kenneth Moreland, Utkarsh Ayachit, Berk Geveci, Kwan-Liu Ma

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Tags: Algorithms, Computer science, Poster, Visualization

October 28, 2011 by hgpu

Parallel Computing the Longest Common Subsequence (LCS) on GPUs: Efficiency and Language Suitability

Amine Dhraief, Raik Issaoui, Abdelfettah Belghith

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Tags: Algorithms, Bioinformatics, Biology, CUDA, nVidia, nVidia GeForce GT 430, OpenCL, Sequence alignment

October 28, 2011 by hgpu

Techniques to maximize memory bandwidth on the Rigel compute accelerator

William J. Tuohy

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Tags: ATI, ATI Radeon HD 5870, Computer science, Memory, nVidia, nVidia GeForce GTX 480, Optimization, Performance, Tesla T10, Thesis

October 27, 2011 by hgpu

Efficient Simulation of Ocean and Land Scenes Based on Digital Earth

Sujun Li, Xiaosu Zhan, Yaoli Wang, Feng Ding

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Tags: 3D Graphics and Realism, Algorithms, ATI, ATI Radeon X600, Computer science, DirectX, Rendering, Visualization

October 27, 2011 by hgpu

Sample distribution shadow maps

Andrew Lauritzen, Marco Salvi, Aaron Lefohn

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Tags: 3D Graphics and Realism, Algorithms, ATI, ATI Radeon HD 5870, Computer science, DirectX, Interactive application, Rendering

October 27, 2011 by hgpu

Self-calibration of geometric and radiometric parameters for cone-beam computed tomography

Wolfgang Wein, Alexander Ladikos and Armin Baumgartner

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Tags: Algorithms, ATI, ATI Radeon HD 5870, Computed tomography, CT, Image reconstruction, Medicine, nVidia, nVidia GeForce GTX 580, Tomography

October 27, 2011 by hgpu

Development of a volume rendering system using 3D texture compression techniques on general-purpose personal computers

Akio Doi, Hiroki Takahashi, Taro Mawatari, Sachio Mega

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Tags: Algorithms, Compression, Computer science, nVidia, nVidia Quadro FX 1800, OpenGL, Programming techniques, Rendering

October 27, 2011 by hgpu

The CUDA implementation of the method of lines for the curvature dependent flows

Tomas Oberhuber, Atsushi Suzuki, Vitezslav Zabka

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Tags: Algorithms, CUDA, Differential equations, Finite volume method, Image processing, Mathematics, nVidia, nVidia GeForce GTX 280, ODEs, Ordinary differential equations

October 27, 2011 by hgpu

CuPBoP-AMD: Extending CUDA to AMD Platforms

CuPBoP: Making CUDA a Portable Language

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

ROCm's implementation of Gromacs

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

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

Experiences with implementing Kokkos’ SYCL backend

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

See all packages

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high performance computing on graphics processing units: hgpu.org

Applications

Matrix inversion speed up with CUDA

Parallel Random Numbers: As Easy as 1, 2, 3

Programming Massively Parallel Processors with CUDA (audio course)

Implementing Domain-Specific Languages for Heterogeneous Parallel Computing

Dax: Data Analysis at Extreme

Parallel Computing the Longest Common Subsequence (LCS) on GPUs: Efficiency and Language Suitability

Techniques to maximize memory bandwidth on the Rigel compute accelerator

Efficient Simulation of Ocean and Land Scenes Based on Digital Earth

Sample distribution shadow maps

Self-calibration of geometric and radiometric parameters for cone-beam computed tomography

Development of a volume rendering system using 3D texture compression techniques on general-purpose personal computers

The CUDA implementation of the method of lines for the curvature dependent flows

Recent source codes

CuPBoP-AMD: Extending CUDA to AMD Platforms

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

ROCm's implementation of Gromacs

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

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

Most viewed papers (last 30 days)