high performance computing on graphics processing units: hgpu.org

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A model of dynamic compilation for heterogeneous compute platforms

Andrew Kerr

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce 9800 GX2, nVidia GeForce GTX 280, OpenCL, PTX, Thesis

June 29, 2013 by hgpu

MATLAB and Python for GPU Computing

Jose Unpingco, Juan Carlos Chaves

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Tags: Computer science, CUDA, Image processing, nVidia, nVidia GeForce 9800 GTS, nVidia GeForce 9800 GX2, Python, Signal processing, Tesla C1060, Tesla C2050

February 15, 2013 by hgpu

An Environment to Support GPU and Multicore Programming for Rapid, High Performance, Application Deployment

James Laurence Brock

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Tags: ATI, ATI Radeon HD 5870, Computer science, CUDA, Heterogeneous systems, Image reconstruction, Monte Carlo simulation, nVidia, nVidia GeForce 9800 GX2, nVidia GeForce GTX 560 Ti, OpenCL, Tesla C1060, Tesla S1070, Thesis

October 26, 2012 by hgpu

Task Performance with List-Mode Data

Luca Caucci

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Tags: Algorithms, Computer science, Data acquisition, Image reconstruction, Medicine, nVidia, nVidia GeForce 9800 GX2, nVidia GeForce GTX 295, Tesla C1060, Tesla C2050, Thesis, Tomography

September 23, 2012 by hgpu

Performance Analysis on Several GPU Architectures of an Algorithm for Noise Removal

M. G. Sanchez, V. Vidal, J. Bataller, G. Verdu

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Tags: Algorithms, CUDA, Image processing, nVidia, nVidia GeForce 9800 GX2, nVidia GeForce GT 120, Signal denoising, Tesla M2050

September 1, 2012 by hgpu

Performance models for CUDA streams on NVIDIA GeForce series

Juan Gomez-Luna, Jose Maria Gonzalez-Linares, Jose Ignacio Benavides, Nicolas Guil

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8800 GTS, nVidia GeForce 9800 GX2, nVidia GeForce GTX 260, nVidia GeForce GTX 280, nVidia GeForce GTX 480, nVidia GeForce GTX 580, Performance

July 9, 2012 by hgpu

Accelerating Sparse Matrix Vector Multiplication on Many-Core GPUs

Weizhi Xu, Zhiyong Liu, Dongrui Fan, Shuai Jiao, Xiaochun Ye, Fenglong Song, Chenggang Yan

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 9800 GX2, nVidia GeForce GTX 295, Performance, Sparse matrix

March 21, 2012 by hgpu

How well do STARLAB and NBODY compare? II: Hardware and accuracy

P. Anders, H. Baumgardt, E. Gaburov, S. Portegies Zwart

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Tags: Algorithms, Astrophysics, Cosmology and Extragalactic Astrophysics, Galaxy Astrophysics, GPU cluster, Instrumentation and Methods for Astrophysics, N-body simulation, nVidia, nVidia GeForce 9800 GX2

January 30, 2012 by hgpu

A New Approach to rCUDA

Jose Duato, Antonio J. Pena, Federico Silla, Juan C. Fernandez, Rafael Mayo, Enrique S. Quintana-Orti

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 9800 GX2, Virtualization

January 23, 2012 by hgpu

Parallel unmixing of remotely sensed hyperspectral images on commodity graphics processing units

Sergio Sanchez, Abel Paz, Gabriel Martin, Antonio Plaza

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Tags: CUDA, Image processing, nVidia, nVidia GeForce 9800 GX2, nVidia GeForce GTX 275, Tesla C1060

January 13, 2012 by hgpu

Methodology of control and supervision of web connected mobile robots with CUDA technology application

Janusz Bedkowski, Andrzej Maslowski

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Tags: Algorithms, Artificial intelligence, Computer science, CUDA, nVidia, nVidia GeForce 9800 GX2, nVidia GeForce GTX 280, nVidia Quadro FX 1600 M, nVidia Quadro FX 3700

December 25, 2011 by hgpu

The Optimization of Algorithms in the Process of Temporal Data Mining Using the Compute Unified Device Architecture

Alexandru Pirjan

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Tags: Algorithms, Computer science, CUDA, Data mining, MapReduce, nVidia, nVidia GeForce 8800 GTS, nVidia GeForce 9800 GX2, nVidia GeForce GTX 280, nVidia GeForce GTX 480, Optimization, Review

November 24, 2011 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

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high performance computing on graphics processing units: hgpu.org

A model of dynamic compilation for heterogeneous compute platforms

MATLAB and Python for GPU Computing

An Environment to Support GPU and Multicore Programming for Rapid, High Performance, Application Deployment

Task Performance with List-Mode Data

Performance Analysis on Several GPU Architectures of an Algorithm for Noise Removal

Performance models for CUDA streams on NVIDIA GeForce series

Accelerating Sparse Matrix Vector Multiplication on Many-Core GPUs

How well do STARLAB and NBODY compare? II: Hardware and accuracy

A New Approach to rCUDA

Parallel unmixing of remotely sensed hyperspectral images on commodity graphics processing units

Methodology of control and supervision of web connected mobile robots with CUDA technology application

The Optimization of Algorithms in the Process of Temporal Data Mining Using the Compute Unified Device Architecture

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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