Download free course Data Parallel C++, pdf file on 565 pages by James Reinders, Ben Ashbaugh, James Brodman, Michael Kinsner, John Pennycook, Xinmin Tian.
Learn how to accelerate C++ programs using data parallelism. This open 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 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.
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 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.
Table of contents
- Introduction
- Where Code Executes
- Data Management
- Expressing Parallelism
- Error Handling
- Unified Shared Memory
- Buffers
- Scheduling Kernels and Data Movement
- Communication and Synchronization
- Defining Kernels
- Vectors
- Device Information
- Practical Tips
- Common Parallel Patterns
- Programming for GPUs
- Programming for CPUs
- Programming for FPGAs
- Libraries
- Memory Model and Atomics
- Future Direction of DPC++
| Pages : | 565 |
| Size : | 15.8 MB |
| Downloads: | 59 |
| Created: | 2022-02-02 |
| License: | CC BY |
| Author(s): | James Reinders, Ben Ashbaugh, James Brodman, Michael Kinsner, John Pennycook, Xinmin Tian |
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