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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

Getting started with GPUs

To begin, you'll need a computer with a discrete GPU installed. For the purposes of this chapter, that will mean a GPU manufactured by NVIDIA. While other brands of GPUs are available, support for those is much weaker, both for general scientific programs, and, in particular, in Julia. Hence, we will not have the opportunity to discuss them in this chapter and we limit our focus to NVIDIA GPUs. 

Once you have access to a computer with a GPU (whether on your desktop or on the cloud), the first order of business is to install a driver and CUDA toolkit on the machine. The exact instructions will depend on your operating system and the GPU model you use. You will need to ensure that the versions of the device driver and CUDA toolkit are compatible.

CUDA is the programming model used by NVIDIA for general-purpose computing on its GPUs. It&apos...
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