Download E-books OpenCL in Action: How to Accelerate Graphics and Computation PDF

By Matthew Scarpino

Summary

OpenCL in Action is an intensive, hands-on presentation of OpenCL, with an eye fixed towards displaying builders tips on how to construct high-performance functions in their personal. It starts via featuring the middle options at the back of OpenCL, together with vector computing, parallel programming, and multi-threaded operations, after which courses you step by step from easy information buildings to advanced functions.

About the Technology

Whatever approach you might have, it most likely has extra uncooked processing energy than you are utilizing. OpenCL is a high-performance programming language that maximizes computational strength by means of executing on CPUs, photos processors, and different number-crunching units. it really is excellent for speed-sensitive initiatives like vector computing, matrix operations, and photographs acceleration.

About this Book

OpenCL in Action blends the idea of parallel computing with the sensible truth of establishing high-performance purposes utilizing OpenCL. It first courses you thru the basic information constructions in an intuitive demeanour. Then, it explains thoughts for high-speed sorting, picture processing, matrix operations, and speedy Fourier remodel. The e-book concludes with a deep examine the all-important topic of portraits acceleration. various demanding examples provide you with other ways to test with operating code.

A heritage in C or C++ is beneficial, yet no previous publicity to OpenCL is needed.

buy of the print ebook comes with a suggestion of a loose PDF, ePub, and Kindle booklet from Manning. additionally on hand is all code from the booklet.

What's Inside

  • Learn OpenCL step by way of step
  • Tons of annotated code
  • Tested algorithms for max performance

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Table of Contents

    PART 1 FOUNDATIONS OF OPENCL PROGRAMMING
  1. Introducing OpenCL
  2. Host programming: primary facts structures
  3. Host programming: information move and partitioning
  4. Kernel programming: information varieties and machine memory
  5. Kernel programming: operators and services
  6. Image processing
  7. Events, profiling, and synchronization
  8. Development with C++
  9. Development with Java and Python
  10. General coding principles
  11. PART 2 CODING sensible ALGORITHMS IN OPENCL
  12. Reduction and sorting
  13. Matrices and QR decomposition
  14. Sparse matrices
  15. Signal processing and the quick Fourier transform
  16. PART three ACCELERATING OPENGL WITH OPENCL
  17. Combining OpenCL and OpenGL
  18. Textures and renderbuffers

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6. 1. 2 Samplers at the host: cl_sampler sooner than a kernel can learn snapshot info, it wishes sure items of knowledge, resembling how the coordinates are formatted and the way to interpret coordinates that transcend the image’s dimension. It must also understand how to interpolate colours among pixel values. All of this knowledge is saved in an information constitution referred to as a sampler. Samplers should be created through the host program or in the kernel. Host purposes create cl_sampler items through calling clCreateSampler, whose signature is as follows: cl_sampler clCreateSampler(cl_context context, cl_bool normalized_coords, cl_addressing_mode addressing_mode, cl_filter_mode filter_mode, cl_int *errcode_ret) the 1st and final parameters are uncomplicated, however the parameters in among are thoroughly new: ■ normalized_coords—Identifies even if coordinates are normalized (given from zero. 0–1. zero) ■ ■ addressing_mode—Identifies how the kernel should still procedure coordinates past the utmost worth filter_mode—Identifies how the kernel interpolates colour values among pixels www. it-ebooks. information 126 bankruptcy 6 picture processing those parameters are very important to appreciate. in case you configure a sampler with the incorrect homes, the kernel will learn the incorrect shades from the picture. NORMALIZED COORDINATES AND NORMALIZED colours quite often, kernels strategy pictures by way of interpreting colour values from a picture item, working at the colour values, and writing the colour values to a moment photograph item. A kernel reads a colour worth at a given element through defining coordinates, and it may well supply those coordinates in a single of 3 methods: ■ ■ ■ Integer—Coordinates are given in a vector of integers from zero to MAX_DIM in every one size. If a picture has dimensions 121 via eighty one, the colour worth on the heart could be accessed with coordinates (60, 40). Floating-point—Coordinates are given in a vector of floats from zero. zero to MAX_DIM in every one size. If a picture has dimensions 121 by means of eighty one, the colour worth on the middle should be accessed with coordinates (60. zero, forty. 0). Normalized floating-point—Coordinates are given in a vector of floats from zero. zero to one. zero in every one size. If a picture has dimensions 121 by way of eighty one, the colour price on the middle will be accessed with coordinates (0. five, zero. 5). Normalized coordinates aren’t a priority whilst coordinates are given as integers, yet they turn into valuable should you are looking to technique a picture with unknown dimensions. this case arises usually for those who paintings with OpenGL textures, that are mentioned in bankruptcy sixteen. To specify that coordinates will be learn in normalized shape, set the second one parameter of clCreateSampler to CL_TRUE. another way, set this parameter to CL_FALSE. It’s an important to grasp the adaptation among normalized coordinates and normalized colours. colour normalization gets rid of the color’s depth point by way of dividing every one part through the sum of the elements. depth is the technical time period for a color’s brightness—color normalization makes colours both brilliant. If a pixel’s elements are given by way of (R, G, B), the normalized parts are computed as follows: R R = -----------------------R+G+B G G = -----------------------R+G+B B B = -----------------------R+G+B After those floating-point values are computed, the consequences are scaled to occupy the pixel’s complete integer diversity.

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