OpenCL编程指南：探索图形处理加速的实战宝典

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OpenCL编程指南是一本专注于OpenCL技术的详细介绍和实践指导书籍，OpenCL（Open Computing Language）是图形处理领域的一个重要标准，它扩展了传统的OpenGL系统，后者原本作为图形硬件软件接口，专用于创建交互式程序，生成动态三维图像，无论是现实主义还是富有创意的视觉效果。OpenGL系列由Addison-Wesley Professional出版，旨在帮助程序员深入理解OpenGL规范，并发掘其全部潜力。 OpenGL系列包含一系列教程和参考书籍，涵盖了从基础入门到高级应用的全方位内容，通过这些资源，读者可以学习如何控制计算机图形技术，实现高质量的图形渲染。OpenCL编程指南由Aaftab Munshi、Benedict R. Gaster、Timothy G. Mattson、James Fung和Dan Ginsburg等作者共同编撰，适合对并行计算和GPU编程感兴趣的开发者使用。与OpenGL主要关注在GPU上的图形渲染不同，OpenCL更注重通用并行计算，可以在多种类型的目标平台上执行，包括CPU、GPU、DSP、FPGA等，提供了一个统一的编程模型，以便开发跨平台的高性能计算应用。OpenCL的优势在于其灵活性和可扩展性，允许开发者编写能够利用硬件并行能力的代码，广泛应用于科学计算、机器学习、图像处理、实时信号处理等领域。本书将涵盖OpenCL的基本概念、编程模型、内存管理、数据类型、算术运算、函数和命令队列等方面，以及如何编写和调试OpenCL程序，同时还会介绍如何优化性能和利用OpenCL库来提高开发效率。此外，书中还可能包括关于如何在实际项目中整合OpenCL和其它编程语言，如C/C++、Python等的实用技巧。对于想要进入并行计算世界或提升现有技能的开发者来说，OpenCL编程指南是不可或缺的参考资料，通过学习和实践，读者将能够设计出高效、可移植且能充分利用现代硬件性能的应用程序。如果你是硬件工程师、计算机科学家或是任何寻求扩展计算能力的开发者，这本书将是你的宝贵工具。

Figures

Figure 1.1 The rate at which instructions are retired is the

same in these two cases, but the power is much less

with two cores running at half the frequency of a

single core. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5

Figure 1.2 A plot of peak performance versus power at the

thermal design point for three processors produced

on a 65nm process technology. Note: This is not to

say that one processor is better or worse than the

others. The point is that the more specialized the

core, the more power-efficient it is. . . . . . . . . . . . . . . . . . . . .6

Figure 1.3 Block diagram of a modern desktop PC with

multiple CPUs (potentially different) and a GPU,

demonstrating that systems today are frequently

heterogeneous . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7

Figure 1.4 A simple example of data parallelism where a

single task is applied concurrently to each element

of a vector to produce a new vector . . . . . . . . . . . . . . . . . . . .9

Figure 1.5 Task parallelism showing two ways of mapping six

independent tasks onto three PEs. A computation

is not done until every task is complete, so the goal

should be a well-balanced load, that is, to have the

time spent computing by each PE be the same. . . . . . . . . .10

Figure 1.6 The OpenCL platform model with one host and

one or more OpenCL devices. Each OpenCL device

has one or more compute units, each of which has

one or more processing elements. . . . . . . . . . . . . . . . . . . . .12

xvi Figures

Figure 1.7 An example of how the global IDs, local IDs, and

work-group indices are related for a two-dimensional

NDRange. Other parameters of the index space are

defined in the figure. The shaded block has a global

ID of (g

, g

) = (6, 5) and a work-group plus local ID of

, w

) = (1, 1) and (l

, l

) =(2, 1) . . . . . . . . . . . . . . . . . . . . . . 16

Figure 1.8 A summary of the memory model in OpenCL and

how the different memory regions interact with

the platform model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .23

Figure 1.9 This block diagram summarizes the components

of OpenCL and the actions that occur on the host

during an OpenCL application. . . . . . . . . . . . . . . . . . . . . . .35

Figure 2.1 CodeBlocks CL_Book project . . . . . . . . . . . . . . . . . . . . . . . .42

Figure 2.2 Using cmake-gui to generate Visual Studio projects . . . . . .43

Figure 2.3 Microsoft Visual Studio 2008 Project . . . . . . . . . . . . . . . . .44

Figure 2.4 Eclipse CL_Book project . . . . . . . . . . . . . . . . . . . . . . . . . . .45

Figure 3.1 Platform, devices, and contexts . . . . . . . . . . . . . . . . . . . . . .84

Figure 3.2 Convolution of an 8×8 signal with a 3×3 filter,

resulting in a 6×6 signal . . . . . . . . . . . . . . . . . . . . . . . . . . .90

Figure 4.1 Mapping get_global_id to a work-item . . . . . . . . . . . . .98

Figure 4.2 Converting a float4 to a ushort4 with round-to-

nearest rounding and saturation . . . . . . . . . . . . . . . . . . . .120

Figure 4.3 Adding two vectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .125

Figure 4.4 Multiplying a vector and a scalar with widening . . . . . . .126

Figure 4.5 Multiplying a vector and a scalar with conversion

and widening . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .126

Figure 5.1 Example of the work-item functions . . . . . . . . . . . . . . . . .150

Figure 7.1 (a) 2D array represented as an OpenCL buffer;

(b) 2D slice into the same buffer . . . . . . . . . . . . . . . . . . . .269

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

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

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

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