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    Julia 1.0 High Performance. Optimizations, distributed computing, multithreading, and GPU programming with Julia 1.0 and beyond - Second Edition

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Autorzy:
    Avik Sengupta, Alan Edelman
    Julia 1.0 High Performance. Optimizations, distributed computing, multithreading, and GPU programming with Julia 1.0 and beyond - Second Edition Avik Sengupta, Alan Edelman - okladka książki

    Julia 1.0 High Performance. Optimizations, distributed computing, multithreading, and GPU programming with Julia 1.0 and beyond - Second Edition Avik Sengupta, Alan Edelman - okladka książki

    Julia 1.0 High Performance. Optimizations, distributed computing, multithreading, and GPU programming with Julia 1.0 and beyond - Second Edition Avik Sengupta, Alan Edelman - audiobook MP3

    Julia 1.0 High Performance. Optimizations, distributed computing, multithreading, and GPU programming with Julia 1.0 and beyond - Second Edition Avik Sengupta, Alan Edelman - audiobook CD

    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    218
    Dostępne formaty:
         PDF
         ePub
         Mobi
    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.

    The book starts with how Julia uses type information to achieve its performance goals, and how to use multiple dispatches to help the compiler emit high-performance machine code. After that, you will learn how to analyze Julia programs and identify issues with time and memory consumption. We teach you how to use Julia's typing facilities accurately to write high-performance code and describe how the Julia compiler uses type information to create fast machine code. Moving ahead, you'll master design constraints and learn how to use the power of the GPU in your Julia code and compile Julia code directly to the GPU. Then, you'll learn how tasks and asynchronous IO help you create responsive programs and how to use shared memory multithreading in Julia. Toward the end, you will get a flavor of Julia's distributed computing capabilities and how to run Julia programs on a large distributed cluster.

    By the end of this book, you will have the ability to build large-scale, high-performance Julia applications, design systems with a focus on speed, and improve the performance of existing programs.

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    O autorach książki

    Avik Sengupta is the Vice President of engineering at Julia Computing, contributor to open source Julia and maintainer of several Julia packages. Avik is the co-founder of two startups in the financial services and AI sectors and 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.
    Those who are new to Julia often ask me what makes it so special, or how it achieves such high performance. Having to think on my feet in order to answer this question has proven challenging. This was the case, at least, until Avik Sengupta came along with the Julia High Performance book. Now all I have to do is tell the enquirers to read the book as it consists of just the right combination of details to answer their questions.
    In an easy-to-read concise set of chapters - most of which contain words like "performance" and "fast" - Avik takes you through examples that you can run yourself in order to see how fast and easy Julia is to use.
    There are computer science words that have become Julia words. It is a pleasure to learn these in a manner that is easy to follow. Just a few examples of this are "JIT", "Multiple Dispatch", "type system", "generated functions", "CUDA", and "SIMD".
    After learning about Julia's design, you will learn to measure performance. From there, you will appreciate Julia's type system. You will then master using arrays along with making fast function calls and fast numbers. Finally, you will learn to write parallel Julia programs.
    With Julia High Performance, you'll pick up the key essentials of Julia in no time. You can then join the friendly, fast growing, online community of Julia programmers.
    Welcome to the world of Julia! Read this book and you will soon join us in loving the Julia language.

    Alan Edelman
    Professor of Applied Mathematics
    Computer Science & AI Labs Member
    MIT
    Co-creator, The Julia Language

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