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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

Thread safety and synchronization primitives

Threads imply code running simultaneously on multiple processor cores in a computer. The processors and the code running within them have access to the entire memory of the computer. This means that code in two threads can attempt to change the same piece of data in memory at the same time. As you can imagine, that would not be a good idea, and many different strategies have been created over the years to deal with this situation in multithreaded code.

Multithreaded Monte Carlo simulation

Remember the code we saw in Chapter 7, Accelerating Code with the GPU, to calculate the value of pi using Monte Carlo simulation. It used a loop to calculate a position based on draws from a random...

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