掌握OpenCL编程：图形硬件的交互指南

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"OpenCL Programming Guide" OpenCL（Open Computing Language）是一种开放标准，用于编写在多种处理器架构上运行的并行程序，包括CPU、GPU（图形处理器）、FPGA（现场可编程门阵列）和其他加速计算设备。该标准由Khronos Group维护，旨在促进跨平台的高效计算，特别是在高性能计算和数据处理领域。 OpenCL编程指南是学习OpenCL开发的重要资源，它涵盖了从基础概念到高级特性的全方位内容。这个指南可能是由Aaftab Munshi、Benedict R. Gaster、Timothy G. Mattson、James Fung和Dan Ginsburg等专家共同编著的，他们都是计算机科学和并行计算领域的权威人士。在OpenCL中，主要的概念有以下几个方面： 1. 平台模型：OpenCL定义了一个层次化的平台模型，允许开发者识别和利用不同类型的硬件资源。平台包含了设备和上下文，设备是执行计算任务的地方，上下文则是管理这些设备的环境。 2. 计算单元：在OpenCL中，计算是在计算单元（如GPU的流处理器或CPU的核心）上执行的。工作项（Work-item）是基本的执行单元，它们被组织成工作组（Work-group），这些工作组在计算单元上并行执行。 3. 编程模型：OpenCL使用C语言的一个子集（称为OpenCL C）来编写内核代码，这些内核可以在设备上执行。内核是并行计算的核心，定义了每个工作项的计算逻辑。 4. 主机与设备交互：主机代码（通常用C++或C编写）负责创建和管理OpenCL对象，如上下文、队列、缓冲区和内核。主机通过命令队列向设备发送任务，并接收结果。 5. 数据传输：OpenCL提供了缓冲区对象来管理主机和设备之间的数据交换。数据可以通过映射、读写命令或事件依赖等方式在主机和设备之间同步。 6. 并行性和内存模型：OpenCL具有丰富的内存层次，包括全局内存、局部内存、私有内存和常量内存，这些内存类型有不同的访问速度和可见性。理解内存模型对于优化计算性能至关重要。 7. 异构计算：OpenCL支持混合架构，允许开发者利用不同类型的设备进行计算，例如同时使用CPU和GPU，实现负载均衡和性能提升。 8. 错误处理和调试：OpenCL提供了一套完整的错误处理机制，通过返回码和异常来报告错误。此外，虽然OpenCL本身不包含强大的调试工具，但有一些第三方工具可以帮助开发者调试OpenCL应用程序。 9. 扩展：OpenCL标准允许硬件供应商提供特定于设备的扩展，以利用设备的独特特性。这些扩展可以通过查询OpenCL平台和设备信息来发现和使用。通过OpenCL Programming Guide，开发者可以深入理解如何利用OpenCL开发高效、可移植的并行应用程序，以充分发挥现代多核和加速计算硬件的潜力。这本书可能还包含大量实例代码、最佳实践以及对OpenCL API的详细解释，帮助读者从理论到实践全面掌握OpenCL编程。

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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

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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

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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

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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

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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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掌握OpenCL编程：图形硬件的交互指南

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OpenCL Programming Guide 2.0

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