This is the code repository for Julia High Performance, published by Packt.
Optimizations, distributed computing, multithreading, and GPU programming with Julia 1.0 and beyond
Julia is a high-level, high-performance dynamic programming language for numerical computing. If you want to understand how to avoid bottlenecks and design your programs for the highest possible performance, then this book is for you.
This book covers the following exciting features:
- Understand how Julia code is transformed into machine code
- Measure the time and memory taken by Julia programs
- Create fast machine code using Julia's type information
- Define and call functions without compromising Julia's performance
- Accelerate your code via the GPU
- Use tasks and async IO for responsive programs
- Run Julia programs on large distributed clusters
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter03.
The code will look like the following:
struct Pixel{T}
x::Int64
y::Int64
color::T
end
Following is what you need for this book: This book is for beginners and intermediate Julia programmers who are interested in high-performance technical programming. A basic knowledge of Julia programming is assumed.
With the following software and hardware list you can run all code files present in the book (Chapter 1-10).
| Chapter | Software required | OS required |
|---|---|---|
| 1-10 | Julia Runtime 1.0 or later | Windows 7 or later 32 or 64 bits, Mac OS 10.8 and later 64 bits or Linux 32 or 64 bits |
Click on the following link to see the Code in Action: Click here to view the videos
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Avik Sengupta is the vice president of engineering at Julia Computing, a contributor to open source Julia, and the maintainer of several Julia packages. Avik is the co-founder of two start-ups in the financial services and AI sectors, and is a creator of large, complex trading systems for the world's leading investment banks. Prior to Julia Computing, Avik was co-founder and CTO at AlgoCircle and at Itellix, director at Lab49, and head of algorithmic solutions at Decimal Point Analytics. Avik earned his MS in computational finance at Carnegie Mellon and MBA Finance at the Indian Institute of Management in Bangalore.
Julia High Performance [Packt]
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