By Benedict Gaster; Lee Howes; David Kaeli; Perhaad Mistry; Dana Schaa
Heterogeneous Computing with OpenCL teaches OpenCL and parallel programming for complicated structures which could comprise various machine architectures: multi-core CPUs, GPUs, and fully-integrated sped up Processing devices (APUs) resembling AMD Fusion know-how. Designed to paintings on a number of structures and with huge help, OpenCL may also help you extra successfully software for a heterogeneous future.
Written via leaders within the parallel computing and OpenCL groups, this e-book provide you with hands-on OpenCL event to handle various basic parallel algorithms. The authors discover reminiscence areas, optimization concepts, pictures interoperability, extensions, and debugging and profiling. meant to help a parallel programming path, Heterogeneous Computing with OpenCL contains special examples all through, plus extra on-line routines and different assisting materials.
Explains ideas and methods to benefit parallel programming with OpenCL, from realizing the 4 abstraction versions to entirely trying out and debugging whole applications.
Covers photograph processing, internet plugins, particle simulations, video enhancing, functionality optimization, and more.
exhibits how OpenCL maps to an instance aim structure and explains a few of the tradeoffs linked to mapping to numerous architectures
Addresses a number basic programming suggestions, with a number of examples and case experiences that display OpenCL extensions for quite a few systems
Read or Download Heterogeneous Computing with OpenCL: Revised OpenCL 1.2 PDF
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Heterogeneous Computing with OpenCL teaches OpenCL and parallel programming for advanced platforms that can contain quite a few machine architectures: multi-core CPUs, GPUs, and fully-integrated sped up Processing devices (APUs) corresponding to AMD Fusion expertise. Designed to paintings on a number of systems and with broad help, OpenCL may help you extra successfully software for a heterogeneous destiny.
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Additional info for Heterogeneous Computing with OpenCL: Revised OpenCL 1.2
The programming task becomes increasingly challenging when faced with the growing parallelism and heterogeneity present in contemporary parallel processors. Given the power and thermal limits of complementary metal-oxide semiconductor (CMOS) technology, microprocessor vendors find it difficult to scale the frequency of these devices to derive more performance and have instead decided to place multiple processors, sometimes specialized, on a single chip. In doing so, the problem of extracting parallelism from an application is left to the programmer, who must decompose the underlying algorithms in the applications and map them efficiently to a diverse variety of target hardware platforms.
6. Mattson TG, Sanders BA, Massingill BL. Patterns for Parallel Programming. Upper Saddle River, NJ: Addison-Wesley; 2004. 7. NVIDA. CUDA Zone. html. 8. AMD. OpenCL Zone. com/openclzone. Chapter 2 Introduction to OpenCL Introduction This chapter introduces OpenCL, the programming fabric that will allow us to weave our application to execute concurrently. Programmers familiar with C and C++ should have little trouble understanding the OpenCL syntax. We begin by reviewing the OpenCL standard. The OpenCL Standard Open programming standards designers are tasked with a very challenging objective: arrive at a common set of programming standards that are acceptable to a range of competing needs and requirements.
A recent report by the International Technology Roadmap for Semiconductors (ITRS) supports this observation and indicates that this trend will continue for the foreseeable future and it will most likely become the most significant factor affecting technology scaling and the future of computer based system. To cope with the expectation of doubling the performance every known period of time (not 2 years anymore), two major changes happened (1) instead of increasing the frequency, modern processors increase the number of cores on each die.