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An unsupervised parallel genetic cluster algorithm for graphics processing units

D. Hendricks, D. Cieslakiewicz, D. Wilcox, T. Gebbie

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Tags: Clustering, Computational finance, CUDA, Genetic programming, nVidia, nVidia GeForce 560 Ti, nVidia GeForce 660 Ti, nVidia GeForce GTX 660 M

March 23, 2014 by hgpu

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

An unsupervised parallel genetic cluster algorithm for graphics processing units

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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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