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Accelerate R by Parallel Technologies
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README.md

README.md

ParallelR

This Repository includes the source code from ParallelR blog in ParallelR.com.

R For Deep Learning

(I): Build Fully Connected Neural Network From Scratch

http://www.parallelr.com/r-deep-neural-network-from-scratch/

  CODE: ParDNN

iris_dnn.R: 1-hidden layer of classification code

Multi_DNN.R: multiple-hidden layers of classificaiton code

iris_dnn_regression.R: 1-hidden layer of regression code

(II): Achieve High-Performance DNN With Parallel Acceleration

http://www.parallelr.com/r-dnn-parallel-acceleration/

CODE: ParDNN {ParDNN.R MINIST_DNN.R}

(III): CUDA and MultiGPUs Acceleration

http://www.parallelr.com/r-dnn-cuda-multigpu/

CODE: ParDNN {ParDNN.R cudaR.R cudaR.cu MultiGPU.R}

R Benchmark For High-Performance Analytics And Computing

(I) Intel Xeon Phi .vs. NVIDIA GPU

http://www.parallelr.com/r-hpac-benchmark-analysis/

(II) Comparision for different GPU Packages (nvBLAS, gputools, gpuR, gmatrix)

http://www.parallelr.com/r-hpac-benchmark-analysis-gpu/

R – GPU Programming for All with ‘gpuR’

http://www.parallelr.com/r-gpu-programming-for-all-with-gpur/

R and OpenMP

(I) Boosting Compiled Code on Multi-core CPUs

http://www.parallelr.com/r-and-openmp-boosting-compiled-code-on-multi-core-cpu-s/

R with Parallel Computing

[中文] http://cos.name/2016/09/r-and-parallel-computing/

[English] http://www.parallelr.com/r-with-parallel-computing/

CODE: PP_for_COS

Parallel Computation with R and xgboost

http://www.parallelr.com/parallel-computation-with-r-and-xgboost/

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