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Julia High Performance

You're reading from   Julia High Performance Optimizations, distributed computing, multithreading, and GPU programming with Julia 1.0 and beyond

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Product type Paperback
Published in Jun 2019
Publisher Packt
ISBN-13 9781788298117
Length 218 pages
Edition 2nd Edition
Languages
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Author (1):
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Avik Sengupta Avik Sengupta
Author Profile Icon Avik Sengupta
Avik Sengupta
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Table of Contents (13) Chapters Close

Preface 1. Julia is Fast FREE CHAPTER 2. Analyzing Performance 3. Types, Type Inference, and Stability 4. Making Fast Function Calls 5. Fast Numbers 6. Using Arrays 7. Accelerating Code with the GPU 8. Concurrent Programming with Tasks 9. Threads 10. Distributed Computing with Julia 11. Licences
12. Other Books You May Enjoy

Shared arrays

Distributed arrays are a fully generic solution that scales across many networked hosts in order to work on data that cannot fit in memory on a single machine. However, in many circumstances, the data does fit in memory, but we would want multiple Julia processes to improve throughput by fully utilizing all cores in a machine. In this situation, shared arrays are useful in terms of getting different Julia processes to operate on the same data.

Shared arrays, as the name suggests, are arrays that are shared across multiple Julia processes on the same machine. They are created by mapping the same region in memory to different processes. Functionality relating to shared arrays lives in the SharedArrays module within base Julia. Constructing a shared array requires specifying its type, its dimensions, and the list of process IDs that will have access to the...

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