Data Parallel C++

Data Parallel C++
PDF, ePUB
  • eBook:
    Data Parallel C++: Mastering DPC++ for Programming of Heterogeneous Systems using C++ and SYCL
  • Author:
    James Reinders, Ben Ashbaugh, James Brodman, Michael Kinsner, John Pennycook, Xinmin Tian
  • Edition:
    1 edition
  • Categories:
  • Data:
    December 3, 2020
  • ISBN:
    1484255739
  • ISBN-13:
    9781484255735
  • Language:
    English
  • Pages:
    574 pages
  • Format:
    PDF, ePUB

Book Description
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++.

Content

Chapter 1: Introduction
Chapter 2: Where Code Executes
Chapter 3: Data Management
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: Communication and Synchronization
Chapter 10: Defining Kernels
Chapter 11: Vectors
Chapter 12: Device Information
Chapter 13: Practical Tips
Chapter 14: Common Parallel Patterns
Chapter 15: Programming for GPUs
Chapter 16: Programming for CPUs
Chapter 17: Programming for FPGAs
Chapter 18: Libraries
Chapter 19: Memory Model and Atomics
Epilogue: Future Direction of DPC++

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