high performance computing on graphics processing units: hgpu.org

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Fast Parallel Machine Learning Algorithms for Large Datasets Using Graphic Processing Unit

Qi Li

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Machine learning, Nearest neighbour, nVidia, Optimization, Sorting, Tesla C1060, Tesla C2050, Tesla C2070, Thesis

December 26, 2011 by hgpu

Theano: A CPU and GPU Math Compiler in Python

James Bergstra, Olivier Breuleux, Frederic Bastien, Pascal Lamblin, Razvan Pascanu, Guillaume Desjardins, Joseph Turian, David Warde-Farley, Yoshua Bengio

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Tags: Algorithms, Computer science, CUDA, Machine learning, nVidia, nVidia GeForce GTX 285, Package, Python

December 12, 2011 by hgpu

Adaboost GPU-based Classifier for Direct Volume Rendering

Oscar Amoros, Sergio Escalera, Anna Puig

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Tags: Computer science, CUDA, GLSL, Machine learning, nVidia GeForce 8800 GTX, nVidia GeForce GTX 470, OpenCL, OpenGL, Rendering, Visualization

November 17, 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

Massively parallel computation using graphics processors with application to optimal experimentation in dynamic control

Sergei Morozov, Sudhanshu Mathur

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Tags: CUDA, Finance, Fortran, Machine learning, nVidia, nVidia GeForce GTX 280, Package

October 21, 2011 by hgpu

OptiML: An implicitly parallel domain-specific language for machine learning

Arvind K. Sujeeth, HyoukJoong Lee, Kevin J. Brown, Tiark Rompf, Hassan Chafi, Michael Wu, Anand R. Atreya, Martin Odersky, Kunle Olukotun

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Tags: Computer science, Heterogeneous systems, Machine learning, nVidia, OpenCL, Optimization, Tesla C2050

October 14, 2011 by hgpu

A Heterogeneous Parallel Framework for Domain-Specific Languages

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

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Tags: Computer science, Heterogeneous systems, Machine learning, nVidia, OpenCL, Optimization, Programming techniques, Tesla C2050

October 14, 2011 by hgpu

Fast On-line Statistical Learning on a GPGPU

FangZhou Xiao, Eric McCreath, Christfried Webers

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Tags: Algorithms, Computer science, CUDA, Machine learning, nVidia, nVidia GeForce GTX 200, nVidia GeForce GTX 295, Stochastic simulation

September 27, 2011 by hgpu

Intelligent GPGPU Classification in Volume Visualization: A framework based on Error-Correcting Output Codes

S. Escalera, A. Puig, O. Amoros, M. Salamo

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Tags: Algorithms, ATI, Computer science, Machine learning, nVidia, nVidia GeForce GTX 470, OpenCL, Rendering, Visualization

September 27, 2011 by hgpu

NIMBLE: a toolkit for the implementation of parallel data mining and machine learning algorithms on mapreduce

Amol Ghoting, Prabhanjan Kambadur, Edwin Pednault, Ramakrishnan Kannan

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Tags: Algorithms, Computer science, Data mining, Machine learning, MapReduce, MPI, OpenCL, Performance, Software Engineering

September 16, 2011 by hgpu

Visual Human – Machine Learning

Raphael Fuchs, Jurgen Waser, Meister Eduard Groller

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Tags: Algorithms, Computer science, CUDA, Genetic programming, Machine learning, nVidia, Pattern recognition, Rendering, Visualization

September 4, 2011 by hgpu

Improving Scheduling Techniques in Heterogeneous Systems with Dynamic, On-Line Optimisations

Marcin Bogdanski, Peter R. Lewis, Tobias Becker, Xin Yao

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Tags: Algorithms, Computer science, Finance, FPGA, Heterogeneous systems, Machine learning, Task scheduling

September 3, 2011 by hgpu

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high performance computing on graphics processing units: hgpu.org

Fast Parallel Machine Learning Algorithms for Large Datasets Using Graphic Processing Unit

Theano: A CPU and GPU Math Compiler in Python

Adaboost GPU-based Classifier for Direct Volume Rendering

Implementing Domain-Specific Languages for Heterogeneous Parallel Computing

Massively parallel computation using graphics processors with application to optimal experimentation in dynamic control

OptiML: An implicitly parallel domain-specific language for machine learning

A Heterogeneous Parallel Framework for Domain-Specific Languages

Fast On-line Statistical Learning on a GPGPU

Intelligent GPGPU Classification in Volume Visualization: A framework based on Error-Correcting Output Codes

NIMBLE: a toolkit for the implementation of parallel data mining and machine learning algorithms on mapreduce

Visual Human – Machine Learning

Improving Scheduling Techniques in Heterogeneous Systems with Dynamic, On-Line Optimisations

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