OpenCL编程指南：异构系统并行编程的开源标准

需积分: 35 148 浏览量更新于2024-07-25 收藏 5.43MB PDF 举报

OpenCL编程指南是一本详尽的教程，由Aaftab Munshi、Benedict R. Gaster、Timothy G. Mattson、James Fung和Dan Ginsburg合著，旨在为软件开发人员提供关于OpenCL这一革命性技术的深入理解。OpenCL（Open Computing Language）是一种开源且免费的标准，它专为异构系统设计，旨在促进并行编程的灵活性和效率。作为通用目的编程平台，OpenCL支持多核处理器（如CPU）、图形处理器（GPU）、Cell架构以及数字信号处理器（DSP），这些硬件的并行处理能力被充分利用，使得开发者能够编写出能在多种平台上运行的高效代码。这不仅限于高性能计算服务器，还包括桌面计算机、移动设备，甚至在游戏、娱乐、科研和医疗等领域都能发挥重要作用。该书覆盖了OpenCL的基础概念、编程模型、数据类型、内存管理、命令队列和算子等核心内容。读者将学习到如何在OpenCL环境中创建并行任务，如何管理和同步工作项，以及如何利用GPU的并行优势来加速计算密集型任务。书中还包含实际示例和案例研究，帮助读者通过实践掌握OpenCL编程技巧。值得注意的是，尽管OpenCL是一个强大工具，但其使用也伴随着一定的风险和限制。作者和出版商虽然尽力确保信息的准确性，但不提供任何形式的明示或默示保证，也不承担因使用书中的信息或程序而产生的偶然或间接损害的责任。对于批量购买或定制封面和内容的企业，出版社提供了优惠条件。 OpenCL编程指南是一本不可或缺的参考资料，无论是对于希望扩展技能的程序员，还是寻求在异构系统上优化性能的工程师，都提供了宝贵的学习资源。通过阅读这本书，读者不仅能掌握OpenCL编程的精髓，还能了解到如何在日益复杂的硬件环境中，利用并行计算技术实现高效、可移植的软件解决方案。

ptg

Figures xvii

Figure 9.1 A failed attempt to use the clEnqueueBarrier()

command to establish a barrier between two

command-queues. This doesn’t work because the

barrier command in OpenCL applies only to the

queue within which it is placed. . . . . . . . . . . . . . . . . . . . . 316

Figure 9.2 Creating a barrier between queues using

clEnqueueMarker() to post the barrier in one

queue with its exported event to connect to a

clEnqueueWaitForEvent() function in the other

queue. Because clEnqueueWaitForEvents()

does not imply a barrier, it must be preceded by an

explicit clEnqueueBarrier(). . . . . . . . . . . . . . . . . . . . .317

Figure 10.1 A program demonstrating OpenCL/OpenGL

interop. The positions of the vertices in the sine

wave and the background texture color values are

computed by kernels in OpenCL and displayed

using Direct3D. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .344

Figure 11.1 A program demonstrating OpenCL/D3D interop.

The sine positions of the vertices in the sine wave

and the texture color values are programmatically

set by kernels in OpenCL and displayed using

Direct3D . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .368

Figure 12.1 C++ Wrapper API class hierarchy . . . . . . . . . . . . . . . . . . .370

Figure 15.1 OpenCL Sobel kernel: input image and output

image after applying the Sobel filter . . . . . . . . . . . . . . . . .409

Figure 16.1 Summary of data in Table 16.1: NV GTX 295 (1 GPU,

2 GPU) and Intel Core i7 performance . . . . . . . . . . . . . . . 419

Figure 16.2 Using one GPU versus two GPUs: NV GTX 295 (1 GPU,

2 GPU) and Intel Core i7 performance . . . . . . . . . . . . . . .420

Figure 16.3 Summary of data in Table 16.2: NV GTX 295 (1 GPU,

2 GPU) and Intel Core i7 performance—10 edges per

vertex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 421

Figure 16.4 Summary of data in Table 16.3: comparison of dual

GPU, dual GPU + multicore CPU, multicore CPU,

and CPU at vertex degree 1 . . . . . . . . . . . . . . . . . . . . . . . .423

ptg

xviii Figures

Figure 17.1 AMD’s Samari demo, courtesy of Jason Yang . . . . . . . . . .426

Figure 17.2 Masses and connecting links, similar to a

mass/spring model for soft bodies . . . . . . . . . . . . . . . . . . .426

Figure 17.3 Creating a simulation structure from a cloth mesh . . . . .427

Figure 17.4 Cloth link structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .428

Figure 17.5 Cloth mesh with both structural links that stop

stretching and bend links that resist folding of the

material . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .428

Figure 17.6 Solving the mesh of a rope. Note how the motion

applied between (a) and (b) propagates during

solver iterations (c) and (d) until, eventually, the

entire rope has been affected. . . . . . . . . . . . . . . . . . . . . . .429

Figure 17.7 The stages of Gauss-Seidel iteration on a set of

soft-body links and vertices. In (a) we see the mesh

at the start of the solver iteration. In (b) we apply

the effects of the first link on its vertices. In (c) we

apply those of another link, noting that we work

from the positions computed in (b). . . . . . . . . . . . . . . . . .432

Figure 17.8 The same mesh as in Figure 17.7 is shown in (a). In

(b) the update shown in Figure 17.7(c) has occurred

as well as a second update represented by the dark

mass and dotted lines. . . . . . . . . . . . . . . . . . . . . . . . . . . . .433

Figure 17.9 A mesh with structural links taken from the

input triangle mesh and bend links created across

triangle boundaries with one possible coloring into

independent batches . . . . . . . . . . . . . . . . . . . . . . . . . . . . .434

Figure 17.10 Dividing the mesh into larger chunks and applying

a coloring to those. Note that fewer colors are

needed than in the direct link coloring approach.

This pattern can repeat infinitely with the same

four colors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 439

Figure 18.1 A single frame from the Ocean demonstration . . . . . . . . .450

ptg

Figures xix

Figure 19.1 A pair of test images of a car trunk being closed.

The first (a) and fifth (b) images of the test

sequence are shown. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 470

Figure 19.2 Optical flow vectors recovered from the test images

of a car trunk being closed. The fourth and fifth

images in the sequence were used to generate this

result. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 471

Figure 19.3 Pyramidal Lucas-Kanade optical flow algorithm . . . . . . .473

Figure 21.1 A matrix multiplication operation to compute

a single element of the product matrix, C. This

corresponds to summing into each element C

i,j

the dot product from the ith row of A with the jth

column of B.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .500

Figure 21.2 Matrix multiplication where each work-item

computes an entire row of the C matrix. This

requires a change from a 2D NDRange of size

1000×1000 to a 1D NDRange of size 1000. We set

the work-group size to 250, resulting in four work-

groups (one for each compute unit in our GPU). . . . . . . .506

Figure 21.3 Matrix multiplication where each work-item

computes an entire row of the C matrix. The same

row of A is used for elements in the row of C so

memory movement overhead can be dramatically

reduced by copying a row of A into private memory. . . . .508

Figure 21.4 Matrix multiplication where each work-item

computes an entire row of the C matrix. Memory

traffic to global memory is minimized by copying

a row of A into each work-item’s private memory

and copying rows of B into local memory for each

work-group. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 510

Figure 22.1 Sparse matrix example . . . . . . . . . . . . . . . . . . . . . . . . . . . .516

Figure 22.2 A tile in a matrix and its relationship with input

and output vectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .520

Figure 22.3 Format of a single-precision 128-byte packet . . . . . . . . . .521

剩余645页未读，继续阅读

暗影剑锋

粉丝: 1
资源: 1

OpenCL编程指南：异构系统并行编程的开源标准

OpenCL Programming Guide - A. Munshi, et al

OpenCL Programming Guide) Aaftab.Munshi .pdf

opencl programming guide

OpenCL Programming Guide 2.0

NVIDIA_OpenCL_ProgrammingGuide

OpenCL_Programming_Guide(NvidiA)

ATI_Stream_SDK_OpenCL_Programming_Guide

AMD_Accelerated_Parallel_Processing_OpenCL_Programming_Guide

VB图像处理工具设计(论文+源代码)(2024uq).7z

【未发表】基于混沌博弈优化算法CGO优化鲁棒极限学习机RELM实现负荷数据回归预测算法研究附Matlab代码.rar

最新资源