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Data parallel C++ : programming accelerated systems using C++ and SYCL / James Reinders, Ben Ashbaugh, James Brodman, Michael Kinsner, John Pennycook, Xinmin Tian ; foreword by Erik Lindahl.

Reinders, James, author.; Ashbaugh, Ben, author.; Brodman, James, author.; Kinsner, Michael, author.; Pennycook, John, author.; Tian, Xinmin, author.
2023
QA76.88
Available Online
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Title
Data parallel C++ : programming accelerated systems using C++ and SYCL / James Reinders, Ben Ashbaugh, James Brodman, Michael Kinsner, John Pennycook, Xinmin Tian ; foreword by Erik Lindahl.
Author
Reinders, James, author.
Edition
Second edition.
ISBN
9781484296912 (electronic bk.)
1484296915 (electronic bk.)
9781484296905
DOI
https://doi.org/10.1007/978-1-4842-9691-2
Published
Berkeley, CA : Apress, [2023]
Copyright
©2023
Language
English
Description
1 online resource (xxx, 630 pages) : illustrations (chiefly color)
Item Number
10.1007/978-1-4842-9691-2 doi
Call Number
QA76.88
Dewey Decimal Classification
004/.35
Summary
"This book, now in is second edition, is the premier resource to learn SYCL 2020 and is the ONLY book you need to become part of this community." Erik Lindahl, GROMACS and Stockholm University Learn how to accelerate C++ programs using data parallelism and SYCL. This open access book enables C++ programmers to be at the forefront of this exciting and important development that is helping to push computing to new levels. This updated second edition is full of practical advice, detailed explanations, and code examples to illustrate key topics. SYCL enables access to parallel resources in modern accelerated heterogeneous systems. Now, a single C++ application can use any combination of devices?including GPUs, CPUs, FPGAs, and ASICs?that are suitable to the problems at hand. This book teaches data-parallel programming using C++ with SYCL and walks through everything needed to program accelerated systems. The book begins by introducing data parallelism and foundational topics for effective use of SYCL. Later chapters cover advanced topics, including error handling, hardware-specific programming, communication and synchronization, and memory model considerations. All source code for the examples used in this book is freely available on GitHub. The examples are written in modern SYCL and are regularly updated to ensure compatibility with multiple compilers. You Will Learn How to: Accelerate C++ programs using data-parallel programming Use SYCL and C++ compilers that support SYCL Write portable code for accelerators that is vendor and device agnostic Optimize code to improve performance for specific accelerators Be poised to benefit as new accelerators appear from many vendors.
Note
Includes index.
Access Note
Open access
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed October 16, 2023).
Added Author
Ashbaugh, Ben, author.
Brodman, James, author.
Kinsner, Michael, author.
Pennycook, John, author.
Tian, Xinmin, author.
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Online Access
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Chapter 1: Introduction
Chapter 2: Where Code Executes
Chapter 3: Data Management and Ordering the Uses of Data
Chapter 4: Expressing Parallelism
Chapter 5: Error Handling
Chapter 6: Unified Shared Memory
Chapter 7: Buffers
Chapter 8: Scheduling Kernels and Data Movement
Chapter 9: Local Memory and Work-group Barriers
Chapter 10: Defining Kernels
Chapter 11: Vector and Math Arrays
Chapter 12: Device Information and Kernel Specialization
Chapter 13: Practical Tips
Chapter 14: Common Parallel Patterns
Chapter 15: Programming for GPUs
Chapter 16: Programming for CPUs
Chapter 17: Programming for FFGAs
Chapter 18: Libraries
Chapter 19: Memory Model and Atomics
Chapter 20: Backend Interoperability
Chapter 21: Migrating CUDA Code
Epilogue.

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