Using the new OpenCL (Open Computing Language) standard, you can write applications that access all available programming resources: CPUs, GPUs, and other processors such as DSPs and the Cell/B.E. processor. Already implemented by Apple, AMD, Intel, IBM, NVIDIA, and other leaders, OpenCL has outstanding potential for PCs, servers, handheld/embedded devices, high performance computing, and even cloud systems. This is the first comprehensive, authoritative, and practical guide to OpenCL 1.1 specifically for working developers and software architects. Written by five leading OpenCL authorities, OpenCL Programming Guide covers the entire specification. It reviews key use cases, shows how OpenCL can express a wide range of parallel algorithms, and offers complete reference material on both the API and OpenCL C programming language. Through complete case studies and downloadable code examples, the authors show how to write complex parallel programs that decompose workloads across many different devices. They also present all the essentials of OpenCL software performance optimization, including probing and adapting to hardware. Coverage includes * Understanding OpenCL's architecture, concepts, terminology, goals, and rationale * Programming with OpenCL C and the runtime API * Using buffers, sub-buffers, images, samplers, and events * Sharing and synchronizing data with OpenGL and Microsoft's Direct3D * Simplifying development with the C++ Wrapper API * Using OpenCL Embedded Profiles to support devices ranging from cellphones to supercomputer nodes * Case studies dealing with physics simulation; image and signal processing, such as image histograms, edge detection filters, Fast Fourier Transforms, and optical flow; math libraries, such as matrix multiplication and high-performance sparse matrix multiplication; and more

Aaftab Munshi，OpenCL核心设计小组的灵魂人物之一，同时他也是OpenCL、OpenGL ES 1.1、OpenGL ES 2.0规范的编辑，与Dan Ginsburg和Dave Shreiner合著了《OpenGL ES 2.0 Programming Guide》。目前他任职于Apple公司。

Benedict R. Gaster，资深软件架构师，他的主要工作是为下一代异构处理器开发编程模型，具体工作是在包含CPU和加速器的新兴处理器（如GPU）上探索并行编程的高层抽象。他为OpenCL的设计做出了巨大贡献，在Khronos Group开放标准协会中他是AMD的代表。由于他在可扩展记录和变量的类型系统方面成绩卓著，因而获得了计算机科学的博士学位。

Timothy G. Mattson，资深并行编程专家，从20世纪80年代中期开始从事相关工作，一直持续到现在。在此期间，他使用过各种类型的并行计算机（矢量超级计算机、SMP、VLIW、NUMA、MPP、集群和多核处理器）。1993年加入Intel公司，专注于OpenCL相关技术的研究。他还是一位资深的技术作家，参与撰写了《Patterns for Parallel Programming》、《An Introduction to Concurrency in Programming Languages》等经典著作。他因自己在分子分散理论方面的工作获得化学博士学位。

James Fung，NVIDIA资深技术工程师，专注于图形硬件上的计算机视觉和图像处理的研究，已经从图形发展到通用计算，经验十分丰富。拥有多伦多大学的电子与计算机工程博士学位，在并行GPU计算机视觉和介导现实领域发表过多篇IEEE和ACM论文。

Dan Ginsburg，资深OpenGL和OpenCL专家，曾经在AMD工作8年，是AMD技术部的高级成员，承担过多种工作，包括开发OpenGL驱动程序、创建桌面和手持3D演示应用，以及领导手持GPU开发工具的开发。他还曾在Still River Systems工作，为Monarch 250质子束放射线治疗系统开发GPU加速图像注册软件。目前在Boston儿童医院的新生儿神经影像和开发科学中心担任首席软件架构师，致力于使用OpenCL来提高神经影像算法的速度。他拥有Worcester Polytechnic学院的计算机科学学士学位和Bentley大学的MBA学位。