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Performance Improvement of Data Mining in Weka through GPU Acceleration

Tiago Augusto Engel, Andrea Schwertner Charao, Manuele Kirsch-Pinheiro, Luiz-Angelo Steffenel

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Tags: Comuter science, CUDA, Data mining, Java, Matrix multiplication, nVidia, nVidia Quadro K 2000, Package, Tesla K20, Tesla M2050

June 13, 2014 by hgpu

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high performance computing on graphics processing units: hgpu.org

Performance Improvement of Data Mining in Weka through GPU Acceleration

Recent source codes

PAstudy

hpx: The C++ Standard Library for Parallelism and Concurrency

OpenDwarfs: a benchmark suite consisting of different computation/communication idioms, i.e., dwarfs, for state-of-art multicore CPUs, GPUs, Intel MICs and Altera FPGAs

Rodinia Benchmark Suite ported for OpenCL-capable Intel FPGAs

The Ocean Tensor Package

Tornado

pocl: Portable Computing Language

TVM: Open deep learning compiler stack for cpu, gpu and specialized accelerators

MyCaffe: A complete deep learning platform written almost entirely in C# for Windows developers

Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks

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International Conference on Image, Video and Signal Processing (IVSP), 2019

International Joint Conference on Signals, Systems and Computers (CSSC), 2018

International Joint Conference on Robotics, Automation and Mechatronics (JCRAM), 2018

International Joint Conference on Robotics and Artificial Intelligence (JCRAI), 2018

International Joint Conference on Computer Vision and Pattern Recognition (CCVPR), 2018