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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

Summary

In this chapter, we covered the performance characteristics in Julia of the most important data structure in scientific computing—the array. We discussed why Julia's design enables extremely fast array operations and how to get the best performance in our code when operating on arrays. This brings us to the end of our journey of creating the fastest possible code in Julia. Using all the tips discussed until now, the performance of your code should approach that of well-written C.

Sometimes, however, this isn't enough, and we want higher performance; our data may be larger or our computations intensive. In this case, the only option is to parallelize our processing using multiple CPUs and systems. In the next chapter, we will take a brief look at the features that Julia provides to write parallel systems easily.

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