high performance computing on graphics processing units: hgpu.org

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Purine: A bi-graph based deep learning framework

Min Lin, Shuo Li, Xuan Luo, Shuicheng Yan

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Tags: Computer science, CUDA, Machine learning, Neural and Evolutionary Computing, nVidia

December 22, 2014 by hgpu

Efficient GPU Implementation for Single Block Orthogonal Dictionary Learning

Paul Irofti

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Tags: Algorithms, ATI, ATI FirePro V8800, Computer science, Neural and Evolutionary Computing, OpenCL

December 18, 2014 by hgpu

MatConvNet – Convolutional Neural Networks for MATLAB

Andrea Vedaldi, Karel Lenc

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Tags: Computer science, CUDA, Machine learning, Mathematical Software, Neural and Evolutionary Computing, Neural networks, nVidia

December 16, 2014 by hgpu

Constructing Long Short-Term Memory based Deep Recurrent Neural Networks for Large Vocabulary Speech Recognition

Xiangang Li, Xihong Wu

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Tags: Computer science, Evolutionary Computations, Neural and Evolutionary Computing, Neural networks, Speech recognition

October 18, 2014 by hgpu

cuDNN: Efficient Primitives for Deep Learning

Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Jonathan Cohen, John Tran, Bryan Catanzaro, Evan Shelhamer

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Tags: Computer science, CUDA, Machine learning, Mathematical Software, Neural and Evolutionary Computing, nVidia, nVidia GeForce GTX 980, Tesla K40

October 8, 2014 by hgpu

A CUDA-Based Real Parameter Optimization Benchmark

Ke Ding, Ying Tan

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Neural and Evolutionary Computing, nVidia, nVidia GeForce GTX 560 Ti, Optimization, Package

July 30, 2014 by hgpu

Efficient On-the-fly Category Retrieval using ConvNets and GPUs

Ken Chatfield, Karen Simonyan, Andrew Zisserman

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Tags: Computer science, Computer vision, CUDA, Machine learning, Neural and Evolutionary Computing, nVidia, nVidia GeForce GTX Titan, Pattern recognition

July 18, 2014 by hgpu

Increasing Deep Neural Network Acoustic Model Size for Large Vocabulary Continuous Speech Recognition

Andrew L. Maas, Awni Y. Hannun, Christopher T. Lengerich, Peng Qi, Daniel Jurafsky, Andrew Y. Ng

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Tags: Computer science, Machine learning, Neural and Evolutionary Computing, Neural networks, Speech recognition

July 1, 2014 by hgpu

One weird trick for parallelizing convolutional neural networks

Alex Krizhevsky

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Tags: Computer science, CUDA, Machine learning, Neural and Evolutionary Computing, nVidia, Tesla K20

April 25, 2014 by hgpu

Multi-GPU parallel memetic algorithm for capacitated vehicle routing problem

Michal Karpinski, Maciej Pacut

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Tags: Computer science, CUDA, Neural and Evolutionary Computing, nVidia, OpenMPI, Tesla S2050

January 23, 2014 by hgpu

GPU Asynchronous Stochastic Gradient Descent to Speed Up Neural Network Training

Thomas Paine, Hailin Jin, Jianchao Yang, Zhe Lin, Thomas Huang

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Tags: Computer science, Computer vision, CUDA, Data parallelism, Neural and Evolutionary Computing, Neural networks, nVidia, Tesla K20

December 24, 2013 by hgpu

libCudaOptimize: an Open Source Library of GPU-based Metaheuristics

Youssef S.G. Nashed, Roberto Ugolotti, Pablo Mesejo, Stefano Cagnoni

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Tags: Artificial intelligence, Computer science, CUDA, Differential evolution, Evolutionary Computations, Metaheuristics, Neural and Evolutionary Computing, nVidia, nVidia GeForce GTS 450, Optimization, Package, Particle swarm optimization

June 21, 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

Purine: A bi-graph based deep learning framework

Efficient GPU Implementation for Single Block Orthogonal Dictionary Learning

MatConvNet – Convolutional Neural Networks for MATLAB

Constructing Long Short-Term Memory based Deep Recurrent Neural Networks for Large Vocabulary Speech Recognition

cuDNN: Efficient Primitives for Deep Learning

A CUDA-Based Real Parameter Optimization Benchmark

Efficient On-the-fly Category Retrieval using ConvNets and GPUs

Increasing Deep Neural Network Acoustic Model Size for Large Vocabulary Continuous Speech Recognition

One weird trick for parallelizing convolutional neural networks

Multi-GPU parallel memetic algorithm for capacitated vehicle routing problem

GPU Asynchronous Stochastic Gradient Descent to Speed Up Neural Network Training

libCudaOptimize: an Open Source Library of GPU-based Metaheuristics

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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