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GPU PARALLEL COMPUTING FOR MACHINE LEARNING IN PYTHON: HOW By Yoshiyasu Takefuji

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Objectstaat
Nieuw: Een nieuw, ongelezen en ongebruikt boek in perfecte staat waarin geen bladzijden ontbreken of ...
ISBN-10
1521524904
Book Title
GPU parallel computing for machine learning in Python: how to
ISBN
9781521524909
Subject Area
Computers
Publication Name
GPU Parallel Computing for Machine Learning in Python : How to Build a Parallel Computer
Item Length
9 in
Publisher
Independently Published
Subject
Neural Networks
Publication Year
2017
Type
Textbook
Format
Trade Paperback
Language
English
Item Height
0.1 in
Author
Yoshiyasu Takefuji
Item Width
6 in
Item Weight
4.5 Oz
Number of Pages
51 Pages

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Product Information

This book illustrates how to build a GPU parallel computer. If you don't want to waste your time for building, you can buy a built-in-GPU desktop/laptop machine. All you need to do is to install GPU-enabled software for parallel computing. Imagine that we are in the midst of a parallel computing era. The GPU parallel computer is suitable for machine learning, deep (neural network) learning. For example, GeForce GTX1080 Ti is a GPU board with 3584 CUDA cores. Using the GeForce GTX1080 Ti, the performance is roughly 20 times faster than that of an INTEL i7 quad-core CPU. We have benchmarked the MNIST hand-written digits recognition problem (60,000 persons: hand-written digits from 0 to 9). The result of MNIST benchmark for machine learning shows that GPU of a single GeForce GTX1080 Ti board takes only less than 48 seconds while the INTEL i7 quad-core CPU requires 15 minutes and 42 seconds. A CUDA core is most commonly referring to the single-precision floating point units in an SM (streaming multiprocessor). A CUDA core can initiate one single precision floating point instruction per clock cycle. CUDA is a parallel computing platform and application programming interface (API) model created by Nvidia. It allows software developers and software engineers to use a CUDA-enabled graphics processing unit (GPU) for general purpose processing. The GPU parallel computer is based on SIMD ( single instruction, multiple data) computing.The first GPU for neural networks was used by Kyoung-Su Oh, et al. for image processing published in 2004 (1). A minimum GPU parallel computer is composed of a CPU board and a GPU board. This book contains the important issue on which CPU/GPU board you should buy and also illustrates how to integrate them in a single box by considering the heat problem. The power consumption of GPU is so large that we should take care of the temperature and heat from the GPU board in the single box. Our goal is to have the faster parallel computer with lower power dissipation. Software installation is another critical issue for machine learning in Python. Two operating system examples including Ubuntu16.04 and Windows 10 system will be described. This book shows how to install CUDA and cudnnlib in two operating systems. Three frameworks including pytorch, keras, and chainer for machine learning on CUDA and cudnnlib will be introduced. Matching problems between operating system (Ubuntu, Windows 10), library (CUDA, cudnnlib), and machine learning framework (pytorch, keras, chainer) are discussed. The paper entitled "GPU" and "open source software" play a key role for advancing deep learning was published in Science (eLetter, July 20 2017)http://science.sciencemag.org/content/357/6346/16/tab-e-letters

Product Identifiers

Publisher
Independently Published
ISBN-10
1521524904
ISBN-13
9781521524909
eBay Product ID (ePID)
247964093

Product Key Features

Author
Yoshiyasu Takefuji
Publication Name
GPU Parallel Computing for Machine Learning in Python : How to Build a Parallel Computer
Format
Trade Paperback
Language
English
Subject
Neural Networks
Publication Year
2017
Type
Textbook
Subject Area
Computers
Number of Pages
51 Pages

Dimensions

Item Length
9 in
Item Height
0.1 in
Item Width
6 in
Item Weight
4.5 Oz

Additional Product Features

Target Audience
Trade

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