Electronic Resource

Data Parallel C++ = Mastering DPC++ for Programming of Heterogeneous Systems using C++ and SYCL

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Learn how to accelerate C++ programs using data parallelism. This open access book enables C++ programmers to be at the forefront of this exciting and important new development that is helping to push computing to new levels. It is full of practical advice, detailed explanations, and code examples to illustrate key topics.

Data parallelism in C++ enables access to parallel resources in a modern heterogeneous system, freeing you from being locked into any particular computing device. Now a single C++ application can use any combination of devices—including GPUs, CPUs, FPGAs and AI ASICs—that are suitable to the problems at hand.

This book begins by introducing data parallelism and foundational topics for effective use of the SYCL standard from the Khronos Group and Data Parallel C++ (DPC++), the open source compiler used in this book. Later chapters cover advanced topics including error handling, hardware-specific programming, communication and synchronization, and memory model considerations.

Data Parallel C++ provides you with everything needed to use SYCL for programming heterogeneous systems.

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Detail Information

Series Title

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Call Number

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Publisher

: Apress Berkeley, CA., 2021

Collation

XXVI, 548hlm; ill., lamp.,

Language

English

ISBN/ISSN

9781484255742

Classification

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Content Type

text

Media Type

computer

Carrier Type

online resource

Edition

1

Subject(s)

Programming Languages
Compilers
Interpreters

Hardware and Maker

Specific Detail Info

Learn heterogenous programming for CPU, GPU, FPGA, ASIC, etc. Gain a vision for the future of parallel programming support in C++ Program with industrial strength implementations of SYCL, with extensions

Statement of Responsibility

James Reinders , Ben Ashbaugh , James Brodman , Michael Kinsner , John Pennycook , Xinmin Tian

Other Information

Cataloger

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Source

https://link.springer.com/book/10.1007/978-1-4842-5574-2

Validator

ida

Digital Object Identifier (DOI)

https://doi.org/10.1007/978-1-4842-5574-2

Journal Volume

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Journal Issue

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Subtitle

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Parallel Title

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