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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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Toc

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

Programming parallel tasks

The low-level facilities examined previously are quite flexible and very powerful. However, they leave a lot to be desired in terms of ease of use. Julia, therefore, has built a set of higher-level programming tools that make it much easier to write parallel code.

The @everywhere macro

The @everywhere macro is used to run the same code in all the processes in the cluster. This is useful for setting up the environment for running the actual parallel computation later. The following code loads the Distributions package and calls the rand method on all the nodes simultaneously:

julia> using Pkg

julia> Pkg.add("Distributions")
...

julia> using Distributions

julia> @everywhere using...
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