Data Parallel C++: Mastering DPC++ for Programming of Heterogeneous Systems Using C++ and SYCL
- 5h 48m
- James Reinders, et al.
- Apress
- 2021
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
About the Author
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).
In this Book
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Introduction
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Where Code Executes
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Data Management
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Expressing Parallelism
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Error Handling
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Unified Shared Memory
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Buffers
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Scheduling Kernels and Data Movement
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Communication and Synchronization
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Defining Kernels
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Vectors
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Device Information
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Practical Tips
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Common Parallel Patterns
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Programming for GPUs
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Programming for CPUs
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Programming for FPGAs
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Libraries
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Memory Model and Atomics
