high performance computing on graphics processing units: hgpu.org

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Accelerating Image Reconstruction in Three-Dimensional Optoacoustic Tomography on Graphics Processing Units

Kun Wang, Chao Huang, Yu-Jiun Kao, Cheng-Ying Chou, Alexander A. Oraevsky, Mark A. Anastasio

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Tags: CUDA, Image reconstruction, Medical Physics, Medicine, nVidia, Physics, Tesla C1060, Tesla C2050, Tomography

April 9, 2013 by hgpu

Creating Optimal Code for GPU-Accelerated CT Reconstruction Using Ant Colony Optimization

Eric Papenhausen, Ziyi Zheng, Klaus Mueller

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Tags: Algorithms, Computed tomography, CT, CUDA, Image processing, Image reconstruction, nVidia, nVidia GeForce GTX 480, Optimization

March 26, 2013 by hgpu

Distributed multi-node, multi-GPU, heterogeneous system for 3D image reconstruction in Electrical Capacitance Tomography – network performance and application analysis

Pawel Kapusta, Michal Majchrowicz, Dominik Sankowski, Lidia Jackowska-Strumillo, Robert Banasiak

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Tags: ATI, ATI Radeon HD 5970, FEM, Finite element method, Heterogeneous systems, Image processing, Image reconstruction, Linear Algebra, Medicine, nVidia, Tesla C2070, Tesla S1070, Tomography

February 9, 2013 by hgpu

Accelerating Image Reconstruction in Dual-Head PET System by GPU and Symmetry Properties

Cheng-Ying Chou, Yun Dong, Yukai Hung, Yu-Jiun Kao, Weichung Wang, Chien-Min Kao, Chin-Tu Chen

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Tags: Algorithms, CUDA, Image processing, Image reconstruction, Medicine, nVidia, PET, Tesla C2070, Tomography

January 20, 2013 by hgpu

Massively parallelizable list-mode reconstruction using a Monte Carlo-based elliptical Gaussian model

G. Sportelli, J. E. Ortuno, J. J. Vaquero, M. Desco, A. Santos

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Tags: Image processing, Image reconstruction, Medicine, Monte Carlo simulation, nVidia, nVidia GeForce GTX 580, OpenCL, PET

January 15, 2013 by hgpu

A Multi-GPU Programming Library for Real-Time Applications

Sebastian Schaetz, Martin Uecker

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Tags: Algorithms, CUDA, GPU cluster, Image reconstruction, Magnetic resonance imaging, MPI, MRI, nVidia, nVidia GeForce GTX 580, Performance, Signal processing

January 8, 2013 by hgpu

FFT Parallel Implementation for MRI Image Reconstruction

Andrei Tugui

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Tags: CUDA, FFT, Image processing, Image reconstruction, MRI, nVidia, nVidia Quadro FX 4400

December 12, 2012 by hgpu

A Computationally Efficient Parallel Kernel Regression for Image Reconstruction

V.Sairam, M.Srinivasa Rao, G.Dada Khalandhar, L.Srikanth, P.K.Baruah, R.R.Sarma

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Tags: Algorithms, CUDA, Image processing, Image reconstruction, Medicine, nVidia, Tesla T20, Tomography

December 8, 2012 by hgpu

Using Graphics Processing Units to Parallelize the FDK Algorithm for Tomographic Image Reconstruction

Joel Sanchez Dominguez, Luiz Fernando de Oliveira, Nilton Alves Junior, Joaquim Teixeira de Assis

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Tags: Computed tomography, CT, CUDA, Image reconstruction, Medicine, nVidia, nVidia GeForce 9400 M, nVidia Quadro FX 2000

November 18, 2012 by hgpu

CUDA and OpenCL Implementations of 3D CT Reconstruction for Biomedical Imaging

Saoni Mukherjee, Nicholas Moore, James Brock, Miriam Leeser

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Tags: ATI, ATI Radeon HD 5870, Computed tomography, CT, CUDA, Heterogeneous systems, Image reconstruction, Medicine, nVidia, OpenCL, Tesla C2070

November 16, 2012 by hgpu

Parallel computation of a SPECT projection operator for a content adaptative mesh model

Francesc Massanes, Jovan G. Brankov

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Tags: Computed tomography, CT, Image reconstruction, Medicine, nVidia, OpenCL, Tesla C2070, Tomography

October 28, 2012 by hgpu

Kernel Specialization for Improved Adaptability and Performance on Graphics Processing Units (GPUs)

Nicholas John Moore

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Tags: Computer science, CUDA, FPGA, Image reconstruction, nVidia, Optimization, Tesis, Tesla C1060, Tesla C2070

October 26, 2012 by hgpu

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

See all packages

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high performance computing on graphics processing units: hgpu.org

Accelerating Image Reconstruction in Three-Dimensional Optoacoustic Tomography on Graphics Processing Units

Creating Optimal Code for GPU-Accelerated CT Reconstruction Using Ant Colony Optimization

Distributed multi-node, multi-GPU, heterogeneous system for 3D image reconstruction in Electrical Capacitance Tomography – network performance and application analysis

Accelerating Image Reconstruction in Dual-Head PET System by GPU and Symmetry Properties

Massively parallelizable list-mode reconstruction using a Monte Carlo-based elliptical Gaussian model

A Multi-GPU Programming Library for Real-Time Applications

FFT Parallel Implementation for MRI Image Reconstruction

A Computationally Efficient Parallel Kernel Regression for Image Reconstruction

Using Graphics Processing Units to Parallelize the FDK Algorithm for Tomographic Image Reconstruction

CUDA and OpenCL Implementations of 3D CT Reconstruction for Biomedical Imaging

Parallel computation of a SPECT projection operator for a content adaptative mesh model

Kernel Specialization for Improved Adaptability and Performance on Graphics Processing Units (GPUs)

Recent source codes

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

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