東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Data parallel C++ = programming acce...

Reinders, James.

Linked to FindBook

Google Book

Amazon

博客來

Data parallel C++ = programming accelerated systems using C++ and SYCL /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data parallel C++/ by James Reinders ... [et al.] ; foreword by Erik Lindahl.
Reminder of title:	programming accelerated systems using C++ and SYCL /
other author:	Reinders, James.
Published:	Berkeley, CA :Apress : : 2023.,
Description:	xxx, 630 p. :ill. (chiefly color), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	C++ (Computer program language) -
Online resource:	https://doi.org/10.1007/978-1-4842-9691-2
ISBN:	9781484296912

Data parallel C++ = programming accelerated systems using C++ and SYCL /
Data parallel C++programming accelerated systems using C++ and SYCL /[electronic resource] :by James Reinders ... [et al.] ; foreword by Erik Lindahl. - Second edition. - Berkeley, CA :Apress :2023. - xxx, 630 p. :ill. (chiefly color), digital ;24 cm.

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.

Open access.

"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.

ISBN: 9781484296912

Standard No.: 10.1007/978-1-4842-9691-2doiSubjects--Topical Terms:

527229
C++ (Computer program language)

LC Class. No.: QA76.88

Dewey Class. No.: 004.35

Data parallel C++ = programming accelerated systems using C++ and SYCL /
LDR:03625nmm a2200349 a 4500 001 2335775
003 DE-He213
005 20231003180929.0
006 m d
007 cr nn 008maaau
008 240402s2023 cau s 0 eng d
020 $a 9781484296912 $q (electronic bk.)
020 $a 9781484296905 $q (paper)
024 7 $a 10.1007/978-1-4842-9691-2 $2 doi
035 $a 978-1-4842-9691-2
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.88
072 7 $a UMC $2 bicssc
072 7 $a COM051010 $2 bisacsh
072 7 $a UMC $2 thema
082 0 4 $a 004.35 $2 23
090 $a QA76.88 $b .D232 2023
245 0 0 $a Data parallel C++ $h [electronic resource] : $b programming accelerated systems using C++ and SYCL / $c by James Reinders ... [et al.] ; foreword by Erik Lindahl.
250 $a Second edition.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2023.
300 $a xxx, 630 p. : $b ill. (chiefly color), digital ; $c 24 cm.
505 0 $a 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.
506 $a Open access.
520 $a "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.
650 0 $a C++ (Computer program language) $3 527229
650 0 $a Heterogeneous computing. $3 778047
650 0 $a OpenCL (Computer program language) $3 1613645
650 1 4 $a Compilers and Interpreters. $3 3592044
650 2 4 $a Maker. $3 3538489
700 1 $a Reinders, James. $3 1641627
700 1 $a Lindahl, Erik. $3 3668424
710 2 $a SpringerLink (Online service) $3 836513
710 2 $a GROMACS. $3 3668425
710 2 $a Stockholm University. $3 3668426
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-1-4842-9691-2
950 $a Professional and Applied Computing (SpringerNature-12059)