high performance computing on graphics processing units: hgpu.org

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Rgtsvm: Support Vector Machines on a GPU in R

Zhong Wang, Tinyi Chu, Lauren A Choate, Charles G Danko

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Tags: Computer science, CUDA, Machine learning, nVidia, Package, R, Tesla K20

June 21, 2017 by hgpu

Kapre: On-GPU Audio Preprocessing Layers for a Quick Implementation of Deep Neural Network Models with Keras

Keunwoo Choi, Deokjin Joo, Juho Kim

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Tags: Computer science, CUDA, Deep learning, Keras, Neural networks, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX Titan X, Package, Python, Signal processing, Tesla K80, Tesla M60

June 21, 2017 by hgpu

Efficient OpenCL-based concurrent tasks offloading on accelerators

A.J. Lazaro-Munoz, J.M. Gonzalez-Linares, J. Gomez-Luna, N. Guil

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Tags: AMD Radeon R9, ATI, Benchmarking, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, OpenCL, Task scheduling, Tesla K20

June 17, 2017 by hgpu

Non-Hydrostatic Pressure Shallow Flows: GPU Implementation Using Finite-Volume and Finite-Difference Scheme

C. Escalante, T. Morales de Luna, M.J. Castro

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Tags: Algorithms, CUDA, Finite difference, Fluid dynamics, Mathematics, Numerical Analysis, nVidia

June 17, 2017 by hgpu

Device Placement Optimization with Reinforcement Learning

Azalia Mirhoseini, Hieu Pham, Quoc V. Le, Benoit Steiner, Rasmus Larsen, Yuefeng Zhou, Naveen Kumar, Mohammad Norouzi, Samy Bengio, Jeff Dean

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Tags: Artificial intelligence, Computer science, CUDA, Deep learning, Heterogeneous systems, Neural networks, nVidia, TensorFlow, Tesla K80

June 17, 2017 by hgpu

Parallel Computing of Particle Trajectory Sonification to Enable Real-Time Interactivity

Jiajun Yang, Thomas Hermann

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Tags: Cluster analysis, nVidia, nVidia GeForce GTX 970, OpenCL, Physics, Vector quantization

June 17, 2017 by hgpu

Crane – Fast and Migratable GPU Passthrough for OpenCL applications

James Gleeson, Daniel Kats, Charlie Mei, Eyal de Lara

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Tags: Computer science, nVidia, nVidia Grid K1, OpenCL, Virtualization

June 10, 2017 by hgpu

Smith-Waterman Acceleration in Multi-GPUs: A Performance per Watt Analysis

Jesus Perez Serrano, Edans Flavius De Oliveira Sandes, Alba Cristina Magalhaes Alves de Melo, Manuel Ujaldon

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Tags: Bioinformatics, Biology, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 980, Performance, Smith-Waterman algorithm

June 10, 2017 by hgpu

Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Priya Goyal, Piotr Dollar, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, Kaiming He

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Tags: Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, Package, Tesla P100

June 10, 2017 by hgpu

MobiRNN: Efficient Recurrent Neural Network Execution on Mobile GPU

Qingqing Cao, Niranjan Balasubramanian, Aruna Balasubramanian

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Tags: Computer science, CUDA, Deep learning, Java, Neural networks, nVidia, Package, RNN, TensorFlow

June 10, 2017 by hgpu

CELES: CUDA-accelerated simulation of electromagnetic scattering by large ensembles of spheres

Amos Egel, Lorenzo Pattelli, Giacomo Mazzamuto, Diederik S. Wiersma, Uli Lemmer

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Tags: Computational Physics, CUDA, Matlab, nVidia, nVidia GeForce GTX 980 Ti, nVidia GeForce GTX Titan X, Optics, Package, Physics

June 10, 2017 by hgpu

Neneta: Heterogeneous Computing Complex-Valued Neural Network Framework

Vladimir Lekic, Zdenka Babic

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Tags: ATI, ATI Radeon HD 5770, Computer science, Deep learning, Heterogeneous systems, Neural networks, OpenCL

June 5, 2017 by hgpu

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Scaling Deep Learning on GPU and Knights Landing clusters

ParTeCL: parallel testing using OpenCL

DenseNet: Densely Connected Convolutional Networks

Memory-Efficient Implementation of DenseNets

SVMbenchmark: CUDA and OpenCL SVM training benchmark

A Comparison of Support Vector Machines Training GPU-Accelerated Open Source Implementations

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high performance computing on graphics processing units: hgpu.org

Programming

Rgtsvm: Support Vector Machines on a GPU in R

Kapre: On-GPU Audio Preprocessing Layers for a Quick Implementation of Deep Neural Network Models with Keras

Efficient OpenCL-based concurrent tasks offloading on accelerators

Non-Hydrostatic Pressure Shallow Flows: GPU Implementation Using Finite-Volume and Finite-Difference Scheme

Device Placement Optimization with Reinforcement Learning

Parallel Computing of Particle Trajectory Sonification to Enable Real-Time Interactivity

Crane – Fast and Migratable GPU Passthrough for OpenCL applications

Smith-Waterman Acceleration in Multi-GPUs: A Performance per Watt Analysis

Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

MobiRNN: Efficient Recurrent Neural Network Execution on Mobile GPU

CELES: CUDA-accelerated simulation of electromagnetic scattering by large ensembles of spheres

Neneta: Heterogeneous Computing Complex-Valued Neural Network Framework

Recent source codes

Scaling Deep Learning on GPU and Knights Landing clusters

Gpufit: GPU-accelerated Levenberg-Marquardt curve fitting in CUDA

GARDENIA: Graph Analytics Repository for Designing Next-Generation Accelerators

GPU-benchmarks

bit-reversal-methods

bifrost: A stream processing framework for high-throughput applications

NEMATUS: Open-Source Neural Machine Translation in Theano

ParTeCL: parallel testing using OpenCL

DenseNet: Densely Connected Convolutional Networks

SVMbenchmark: CUDA and OpenCL SVM training benchmark

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Featured events

International Conference on Intelligent Autonomous Systems (ICIAS), 2018

International Conference on Robotics and Intelligent System (ICRIS), 2018

International Conference on Image and Graphics Processing (ICIGP), 2018

8th International Conference on Bioscience, Biochemistry and Bioinformatics (ICBBB), 2018

The 10th International Conference on Computer Modeling and Simulation (ICCMS), 2018