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oapen-20.500.12657-429172020-11-14T01:46:33Z Data Parallel C++ Reinders, James Ashbaugh, Ben Brodman, James Kinsner, Michael Pennycook, John Tian, Xinmin Programming Languages, Compilers, Interpreters Hardware and Maker Maker heterogenous FPGA programming GPU programming Parallel programming Data parallelism SYCL Intel One API Programming & scripting languages: general Compilers & interpreters Computer hardware bic Book Industry Communication::U Computing & information technology::UM Computer programming / software development::UMX Programming & scripting languages: general bic Book Industry Communication::U Computing & information technology::UK Computer hardware 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++. 2020-11-13T13:35:04Z 2020-11-13T13:35:04Z 2021 book ONIX_20201113_9781484255742_23 https://library.oapen.org/handle/20.500.12657/42917 eng application/pdf n/a 2021_Book_DataParallelC.pdf https://www.springer.com/9781484255742 Springer Nature Apress 10.1007/978-1-4842-5574-2 10.1007/978-1-4842-5574-2 6c6992af-b843-4f46-859c-f6e9998e40d5 Apress 548 open access
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English
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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. 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++.
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2021_Book_DataParallelC.pdf
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2021_Book_DataParallelC.pdf
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2021_Book_DataParallelC.pdf
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2021_book_dataparallelc.pdf
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Springer Nature
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2020
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https://www.springer.com/9781484255742
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1771297463840276480
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