Titlebook: Data Parallel C++; Programming Accelera James Reinders,Ben Ashbaugh,Xinmin Tian Book‘‘‘‘‘‘‘‘ 2023Latest edition Intel Corporation 2023 hete

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終端

Amanda Laugesen,Richard GehrmannChapter 15 describes considerations to keep in mind when targeting GPUs using C++ with SYCL. It explains how a typical GPU works, how GPU software and hardware execute a SYCL application, and tips and techniques to keep in mind when writing and optimizing parallel kernels for a GPU.

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Richard Gehrmann,Amanda LaugesenChapter 17 raises considerations to keep in mind when targeting FPGAs using C++ with SYCL. It describes mappings from device code to FPGA devices, how FPGA software and hardware execute a SYCL application, and tips and techniques to keep in mind when?writing and optimizing parallel kernels for an FPGA.

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Data Management, Chapter 3 discusses methods to manage data in our quest to exploit data parallelism for performance gains. It introduces Unified Shared Memory (USM) and the higher-level buffer abstraction and explores when to use each method to manage the data in our accelerated programs.

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Unified Shared Memory,Chapter 6 discusses Unified Shared Memory (USM) in detail. USM is a pointer-based strategy for data management. This chapter describes the three types of USM allocations, how to allocate and deallocate USM memory, and how different types of USM data movement can be explicitly in our applications or implicitly controlled by the system.