探索OpenCL编程：GPU计算指南 - CSDN文库

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"OpenCL Programming Guide 是一本关于OpenCL编程的指南，由Aaftab Munshi、Benedict R. Gaster、Timothy G. Mattson、James Fung和Dan Ginsburg等作者编写，旨在帮助程序员深入理解和充分利用OpenCL标准。这本书是OpenGL系列的一部分，该系列由Addison-Wesley Professional出版，提供了对OpenGL标准的教程和参考内容。通过访问informit.com/opengl可以获取完整的OpenGL相关产品列表。" OpenCL（Open Computing Language）是一个开放标准，用于跨异构计算平台进行并行编程。它允许开发人员编写程序，使硬件如GPU（图形处理器）、CPU和FPGA（现场可编程门阵列）等能够协同工作，以解决复杂的计算问题。OpenCL的目标是提供一个统一的编程模型，使得开发者能够利用各种设备的计算能力。本书《OpenCL Programming Guide》涵盖了OpenCL的基础知识，包括： 1. **OpenCL架构**：介绍OpenCL的架构设计，包括主机（Host）和设备（Device）的概念，以及它们之间的通信机制。 2. **OpenCL环境设置**：讲解如何在不同操作系统上安装和配置OpenCL运行时环境。 3. **基础数据类型和内存模型**：讨论OpenCL支持的数据类型，如基本整型、浮点型和向量类型，并解释内存层次结构，如全局内存、局部内存和私有内存。 4. **计算上下文（Context）**：定义计算上下文，它是OpenCL对象（如队列、缓冲区和程序）的容器，用于管理设备间的交互。 5. **命令队列（Command Queue）**：描述如何创建和管理命令队列，以调度和执行设备上的运算任务。 6. **内核（Kernel）**：讲解OpenCL的核心概念——内核，这是在设备上执行的并行代码段。包括内核的声明、定义、编译和执行。 7. **缓冲区和映射（Buffer and Mapping）**：阐述如何在主机和设备之间传输数据，以及如何使用缓冲区和映射功能优化数据交换。 8. **并行计算模式**：介绍OpenCL中的并行计算模型，如工作项（Work-item）、工作组（Work-group）和计算网格（Work-dimensions）。 9. **同步和原子操作**：讲解如何实现设备上的同步，以及使用原子操作来处理共享数据。 10. **事件和依赖**：探讨如何通过事件来控制任务的执行顺序，以及创建依赖关系以确保正确的执行顺序。 11. **性能优化**：提供关于如何提高OpenCL程序性能的指导，包括优化内核代码、内存访问模式和数据布局等。 12. **调试和分析工具**：介绍可用的调试和性能分析工具，帮助开发者找出并解决问题。 13. **OpenCL C++绑定**：讨论现代C++与OpenCL的集成，如使用C++11标准库和模板来增强OpenCL编程体验。 14. **案例研究**：通过实际示例来展示OpenCL在图像处理、物理模拟、机器学习等领域的应用。通过阅读《OpenCL Programming Guide》，读者将不仅掌握OpenCL的基本概念，还能获得实践经验，从而能够在各种异构计算平台上编写高效并行代码，充分利用硬件资源，提升应用程序的性能。

xv

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

x

, g

y

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

(w

x

, w

y

) = (1, 1) and (l

x

, l

y

) =(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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