Data Parallel C++ / James Reinders  

Public ISBD Titre : Data Parallel C++ : Mastering DPC++ for Programming of Heterogeneous Systems using C++ and SYCL Type de document : document électronique Auteurs : James Reinders, Auteur ; Ben Ashbaugh, Auteur ; James Brodman, Auteur Editeur : Berlin [Germany] : Springer Nature Limited Année de publication : 2021 Importance : 548 p. Présentation : ill. ISBN/ISSN/EAN : 978-1-4842-5574-2 Langues : Anglais (eng) Tags : heterogenous  FPGA programming  GPU programming  Parallel programming  Data parallelism  SYCL  Intel One API Index. décimale : 005.133 Langages de programmation particuliers Résumé : 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. En ligne : https://doi.org/10.1007/978-1-4842-5574-2 Data Parallel C++ : Mastering DPC++ for Programming of Heterogeneous Systems using C++ and SYCL [document électronique] / James Reinders, Auteur ; Ben Ashbaugh, Auteur ; James Brodman, Auteur . - Berlin (Germany) : Springer Nature Limited, 2021 . - 548 p. : ill. ISBN : 978-1-4842-5574-2 Langues : Anglais (eng) Tags : heterogenous  FPGA programming  GPU programming  Parallel programming  Data parallelism  SYCL  Intel One API Index. décimale : 005.133 Langages de programmation particuliers Résumé : 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. En ligne : https://doi.org/10.1007/978-1-4842-5574-2

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Data Parallel C++ / James Reinders  

Public ISBD Titre : Data Parallel C++ : Programming Accelerated Systems Using C++ and SYCL Type de document : document électronique Auteurs : James Reinders, Auteur ; Ben Ashbaugh, Auteur ; James Brodman, Auteur Editeur : Berlin [Germany] : Springer Nature Limited Année de publication : 2023 Importance : 630 p. Présentation : ill. ISBN/ISSN/EAN : 978-1-4842-9691-2 Langues : Anglais (eng) Tags : heterogenous  FPGA programming  GPU programming  Parallel programming  Data parallelism  SYCL  Intel One API Index. décimale : 005.133 Langages de programmation particuliers Résumé : 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. En ligne : https://doi.org/10.1007/978-1-4842-9691-2 Data Parallel C++ : Programming Accelerated Systems Using C++ and SYCL [document électronique] / James Reinders, Auteur ; Ben Ashbaugh, Auteur ; James Brodman, Auteur . - Berlin (Germany) : Springer Nature Limited, 2023 . - 630 p. : ill. ISBN : 978-1-4842-9691-2 Langues : Anglais (eng) Tags : heterogenous  FPGA programming  GPU programming  Parallel programming  Data parallelism  SYCL  Intel One API Index. décimale : 005.133 Langages de programmation particuliers Résumé : 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. En ligne : https://doi.org/10.1007/978-1-4842-9691-2

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