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Java for Data Science

You're reading from   Java for Data Science Examine the techniques and Java tools supporting the growing field of data science

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Product type Paperback
Published in Jan 2017
Publisher Packt
ISBN-13 9781785280115
Length 386 pages
Edition 1st Edition
Languages
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Authors (2):
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Jennifer L. Reese Jennifer L. Reese
Author Profile Icon Jennifer L. Reese
Jennifer L. Reese
Richard M. Reese Richard M. Reese
Author Profile Icon Richard M. Reese
Richard M. Reese
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Toc

Table of Contents (13) Chapters Close

Preface 1. Getting Started with Data Science 2. Data Acquisition FREE CHAPTER 3. Data Cleaning 4. Data Visualization 5. Statistical Data Analysis Techniques 6. Machine Learning 7. Neural Networks 8. Deep Learning 9. Text Analysis 10. Visual and Audio Analysis 11. Mathematical and Parallel Techniques for Data Analysis 12. Bringing It All Together

Using Aparapi

Aparapi (https://github.com/aparapi/aparapi) is a Java library that supports concurrent operations. The API supports code running on GPUs or CPUs. GPU operations are executed using OpenCL, while CPU operations use Java threads. The user can specify which computing resource to use. However, if GPU support is not available, Aparapi will revert to Java threads.

The API will convert Java byte codes to OpenCL at runtime. This makes the API largely independent from the graphics card used. The API was initially developed by AMD but has been released as open source. This is reflected in the basic package name, com.amd.aparari. Aparapi offers a higher level of abstraction than provided by OpenCL.

Aparapi code is located in a class derived from the Kernel class. Its execute method will start the operations. This will result in an internal call to a run method, which needs to be overridden. It is within the run method that concurrent code is placed. The run method is executed multiple...

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