high performance computing on graphics processing units: hgpu.org

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

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

Combining approximate inference methods for efficient learning on large computer clusters

Parallel mutual information estimation for inferring gene regulatory networks on GPUs

Torch7: A Matlab-like Environment for Machine Learning

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

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

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