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Students will come away from this text with hands-on experience and significant knowledge of the syntax and use of OpenCL to address a range of fundamental parallel algorithms. Designed to work on multiple platforms and with wide industry support, OpenCL will help you more effectively program for a heterogeneous future. Written by leaders in the parallel computing and OpenCL communities, Heterogeneous Computing with OpenCL explores memory spaces, optimization techniques, graphics interoperability, extensions, and debugging and profiling.

It includes detailed examples throughout, plus additional online exercises and other supporting materials that can be downloaded at http: Gaster is a software architect working on programming models for next-generation heterogeneous processors, in particular looking at high-level abstractions for parallel programming on the emerging class of processors that contain both CPUs and accelerators such as GPUs.

Heterogeneous Computing with OpenCL : Benedict Gaster :

Benedict has a Ph. D in computer science for his work on type systems for extensible records and variants.

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Lee Howes has spent the last two years working at AMD and currently focuses on programming models for the future of heterogeneous computing. Lee's interests lie in declaratively representing mappings of iteration domains to data and in communicating complicated architectural concepts and optimizations succinctly to a developer audience, both through programming model improvements and education.

Lee has a Ph. Kaeli has co-authored more than critically reviewed publications.

Heterogeneous Computing with OpenCL

His research spans a range of areas including microarchitecture to back-end compilers and software engineering. He leads a number of research projects in the area of GPU Computing. He is presently focused on debugger architectures for upcoming platforms shared memory and discrete Graphics Processing Unit GPU platforms.


He has enjoyed implementing medical imaging algorithms for GPGPU platforms and architecture aware data structures for surgical simulators. Perhaad's present work focuses on the design of debuggers and architectural support for performance analysis for the next generation of applications that will target GPU platforms. Lists with This Book.

This book is not yet featured on Listopia. Jun 25, Rodrigo Nemmen rated it liked it.

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First a few words about my background: I have considerable experience in Python, C and Fortran 90 for scientific applications. I never wrote codes exploiting GPUs, and I have a very clear goal: I have been reading several online tutorials and searching for books that are helpful for beginners with GPU programming.

Th First a few words about my background: This book is clearly geared towards programmers with experience in C and parallel programming e. It is not very pedagogical. It reads more like a reference book rather than a tutorial.

Here is the structure of the book: Several different optimizations are tried out and their relative speedup is presented however no comparison with multi-core CPU is presented Here is what I think is missing: Mehmet Oguz Derin is currently reading it Jul 09, Brian Sletten is currently reading it Dec 23, Hannes Sowa added it Jun 24, David marked it as to-read Oct 13, There are no discussion topics on this book yet.