By Perhaad Mistry and Dana Schaa (Auth.)
Heterogeneous Computing with OpenCL teaches OpenCL and parallel programming for advanced structures which may contain various equipment architectures: multi-core CPUs, GPUs, and fully-integrated sped up Processing devices (APUs) equivalent to 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 through leaders within the parallel computing and OpenCL groups, this booklet provide you with hands-on OpenCL adventure to deal with a variety of basic parallel algorithms. The authors discover reminiscence areas, optimization ideas, pics interoperability, extensions, and debugging and profiling. meant to help a parallel programming path, Heterogeneous Computing with OpenCL contains precise examples all through, plus extra on-line workouts and different assisting materials.
- Explains ideas and methods to profit parallel programming with OpenCL, from figuring out the 4 abstraction versions to entirely trying out and debugging entire applications.
- Covers photo processing, internet plugins, particle simulations, video enhancing, functionality optimization, and more.
- Shows how OpenCL maps to an instance goal structure and explains the various tradeoffs linked to mapping to varied architectures
- Addresses quite a number primary programming innovations, with a number of examples and case reports that display OpenCL extensions for a number of platforms
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Heterogeneous Computing with OpenCL teaches OpenCL and parallel programming for advanced platforms which can comprise quite a few equipment architectures: multi-core CPUs, GPUs, and fully-integrated speeded up Processing devices (APUs) similar to AMD Fusion know-how. Designed to paintings on a number of structures and with vast help, OpenCL can help you extra successfully software for a heterogeneous destiny.
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Additional info for Heterogeneous Computing with Open: CL
A few more steps are required before the kernel can actually be executed. Unlike calling functions in regular C programs, we cannot simply call a kernel by providing a list of arguments. 27 28 CHAPTER 2 Introduction to OpenCL Executing a kernel requires dispatching it through an enqueue function. Due both to the syntax of the C language and to the fact that kernel arguments are persistent (and hence we need not repeatedly set them to construct the argument list for such a dispatch), we must specify each kernel argument individually using the function clSetKernelArg().
This data needs to be physically present on a device before execution can begin. 23 24 CHAPTER 2 Introduction to OpenCL In order for data to be transferred to a device, it must first be encapsulated as a memory object. OpenCL defines two types of memory objects: buffers and images. Buffers are equivalent to arrays in C, created using malloc(), where data elements are stored contiguously in memory. Images, on the other hand, are designed as opaque objects, allowing for data padding and other optimizations that may improve performance on devices.
For a long time, voltages could reduce with each process generation such that frequency scaling would not increase the power consumption uncontrollably. However, as process technology has reached the small sizes we see today, we can no longer scale the voltage down without increasing the error rate of transistor switching and hence frequency scaling requires voltage increases. The increase in power from any increase in frequency is then substantial. 43 44 CHAPTER 3 OpenCL device architectures As a second problem, increasing clock frequency on-chip requires either increasing off-chip memory bandwidth to provide data fast enough to not stall the linear workload running through the processor or increasing the amount of caching in the system.