Data Parallel C++: Mastering DPC++ for Programming of Heterogeneous Systems using C++ and SYCL - Softcover

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

Softcover

ISBN 10: 1484255739 ISBN 13: 9781484255735

Verlag: Apress, 2020

Inhaltsangabe

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.

What You'll Learn

• Accelerate C++ programs using data-parallel programming
• Target multiple device types (e.g. CPU, GPU, FPGA)
• Use SYCL and SYCL compilers 
• Connect with computing’s heterogeneous future via Intel’s oneAPI initiative

Who This Book Is For

Those new data-parallel programming and computer programmers interested in data-parallel programming using C++.

Über diesen Titel

Über die Autorin bzw. den Autor

James Reinders is a consultant with more than three decades experience in Parallel Computing, and is an author/co-author/editor of nine technical books related to parallel programming.  He has had the great fortune to help make key contributions to two of the world's fastest computers (#1 on Top500 list) as well as many other supercomputers, and software developer tools. James finished 10,001 days (over 27 years) at Intel in mid-2016, and now continues to write, teach, program, and do consulting in areas related to parallel computing (HPC and AI).  

Von der hinteren Coverseite

Learn how to accelerate C++ programs using data parallelism. 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 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. This book teaches data-parallel programming using C++ and the SYCL standard from the Khronos Group and walks through everything needed to use SYCL for programming heterogeneous systems. The book begins by introducing data parallelism and foundational topics for effective use of SYCL 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. You will learn: • How to accelerate C++ programs using data-parallel programming • How to target multiple device types (e.g. CPU, GPU, FPGA) • How to use SYCL and SYCL compilers • How to connect with computing’s heterogeneous future via Intel’s oneAPI initiative

Details zur Ausgabe

• Verlag: Apress
• Erscheinungsdatum: 2020
• Sprache: Englisch
• ISBN 10: 1484255739
• ISBN 13: 9781484255735
• Einband: Taschenbuch
• Auflage: 1
• Anzahl der Seiten: 574
• Kontakt zum Hersteller: Nicht verfügbar
• Verantwortliche Person: Nicht verfügbar