high performance computing on graphics processing units: hgpu.org

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Fast Endmember Extraction for Massive Hyperspectral Sensor Data on GPUs

Zebin Wu, Shun Ye, Jie Wei, Zhihui Wei, Le Sun, Jianjun Liu

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Tags: CUDA, Image processing, nVidia, nVidia Quadro 600, Sensing, Tesla C2050

September 22, 2013 by hgpu

Paralleling Variable Block Size Motion Estimation of HEVC on Multi- Core CPU Plus GPU Platform

Xiang-wen Wang, Li Song, Min Chen, Jun-jie Yang

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Tags: Compression, CUDA, H.264/AVC, Image processing, Motion compensation, nVidia, Tesla C2050

September 22, 2013 by hgpu

Adjustable GPU Acceleration for Hermitian Eigensystems

Michael T. Garba, Horacio Gonzalez-Velez, Daniel L. Roach

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Tags: Computer science, CUDA, Linear Algebra, nVidia, Tesla C2050

September 18, 2013 by hgpu

A Novel CPU/GPU Simulation Environment for Large-Scale Biologically-Realistic Neural Modeling

Roger V. Hoang, Devyani Tanna, Laurence C. Jayet Bray, Sergiu M. Dascalu, Frederick C. Harris Jr.

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Tags: Biology, CUDA, Neural networks, Neuroscience, nVidia, nVidia GeForce GTX 460, nVidia GeForce GTX 480, nVidia GeForce GTX 680, Package, Tesla C2050

September 14, 2013 by hgpu

GPU-based simulation of the long-range Potts model via parallel tempering

Attila Boer

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Tags: Computational Physics, Condensed matter, CUDA, nVidia, Package, Statistical Mechanics, Tesla C2050

August 27, 2013 by hgpu

Estimating the WCET of GPU-Accelerated Applications using Hybrid Analysis

Adam Betts, Alastair Donaldson

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Tags: Computer science, CUDA, GPGPU-sim, nVidia, OpenCL, Package, Python, Tesla C2050

August 26, 2013 by hgpu

Towards a Distributed GPU-Accelerated Matrix Inversion

Gerardo Ares, Pablo Ezzatti, Enrique S. Quintana-Orti

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Tags: Algorithms, Computer science, CUDA, Factorization, Matrix inversion, nVidia, Tesla C2050, Tesla M2050

August 18, 2013 by hgpu

A GPU implementation for improved granular simulations with LAMMPS

Emmanuel N. Millan, Christian Ringl, Carlos S. Bederian, Maria Fabiana Piccoli, Carlos Garcia Garino, Herbert M. Urbassek, Eduardo M. Bringa

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Tags: CUDA, Molecular dynamics, nVidia, Performance, Physics, Tesla C2050, Tesla C2075, Tesla M2090

August 18, 2013 by hgpu

Scalable Dense Linear Algebra on Heterogeneous Hardware

George Bosilca, Aurelien Bouteiller, Anthony Danalis, Thomas Herault, Jakub Kurzak, Piotr Luszczek, Stanimire Tomov, Jack J. Dongarra

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, Linear Algebra, nVidia, nVidia GeForce GTX 280, Tesla C2050

July 30, 2013 by hgpu

Harnessing the GPU for Real-Time Haptic Tissue Simulation

C. E. Etheredge, E. E. Kunst, A. J. B. Sanders

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Tags: CUDA, Medicine, nVidia, Tesla C2050

July 29, 2013 by hgpu

Point Based Approximate Color Bleeding With Cuda

Nicholas D. Feeney

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Tags: 3D Graphics and Realism, Algorithms, Computer science, CUDA, nVidia, Tesla C2050, Thesis

July 29, 2013 by hgpu

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

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

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

See all packages

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high performance computing on graphics processing units: hgpu.org

Fast Endmember Extraction for Massive Hyperspectral Sensor Data on GPUs

Paralleling Variable Block Size Motion Estimation of HEVC on Multi- Core CPU Plus GPU Platform

Adjustable GPU Acceleration for Hermitian Eigensystems

A Novel CPU/GPU Simulation Environment for Large-Scale Biologically-Realistic Neural Modeling

GPU-based simulation of the long-range Potts model via parallel tempering

Estimating the WCET of GPU-Accelerated Applications using Hybrid Analysis

Towards a Distributed GPU-Accelerated Matrix Inversion

A GPU implementation for improved granular simulations with LAMMPS

Scalable Dense Linear Algebra on Heterogeneous Hardware

Harnessing the GPU for Real-Time Haptic Tissue Simulation

Point Based Approximate Color Bleeding With Cuda

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

Recent source codes

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

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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