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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

SIMD parallelization (AVX2, AVX512)

Single Instruction, Multiple Data (SIMD) is method of parallelizing computation within the CPU, whereby a single operation is performed on many data elements simultaneously. Modern CPU architectures contain instruction sets that can do this, operating on many variables at once.

On Intel processors, these types of instructions have been progressively implemented using names such as SSE, AVX2, and AVX512. Each of these implementations add on extra functionality, but also allow operations on wider data. SIMD was first implemented in older Intel processors, with the name SSE, which went through multiple versions. Most Intel and AMD processors from the last decade implement the AVX2 instruction set, which provides 256 bits of parallelism. More recent processors have an upgraded instruction set called AVX512, which, as the name suggests, can operate...

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