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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

Writing generic library functions with arrays

The suggestions in the previous sections should make your array code fast and high performance. If you are directly writing code to solve your own problems, this should be enough. However, if you are writing library routines that may be called by other programs, you will need to heed additional concerns. Your function may be called with arrays of different kinds and with different dimensions. To write generic code that is fast with all custom types built-in, and for arrays of many dimensions, you need to be careful in how you iterate over the elements of the arrays.

All Julia arrays are subtypes of the AbstractArray type. All abstract arrays must provide facilities for indexation and iteration. However, these can be implemented very differently for different types of arrays. The default array is DenseArray, which stores its elements...

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