OpenCL编程指南：深入理解并行计算

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"OpenCL Programming Guide" OpenCL（Open Computing Language）编程指南是针对图形硬件的软件接口——OpenGL系统的一个重要补充。OpenGL主要用于创建交互式的三维图像程序，它赋予开发者控制计算机图形技术的能力，以便生成逼真的图片或者创造富有创意的非现实场景。 OpenCL是一个开放标准，旨在提供一种跨平台的编程框架，用于高效地执行计算密集型任务，特别是在并行处理硬件如GPU（图形处理器）上。OpenCL的目标是让开发者能够利用各种设备的计算能力，包括CPU、GPU、FPGA以及数字信号处理器(DSP)等。它由Khronos Group维护，与OpenGL一起构成了现代高性能计算的重要组成部分。 OpenCL编程指南通常会涵盖以下几个关键知识点： 1. **基础概念**：包括OpenCL架构、上下文(Context)、命令队列(Command Queue)、设备(Device)、平台(Platform)等核心概念的介绍，这些构成了OpenCL程序的基础结构。 2. **数据并行性**：OpenCL支持数据并行处理，允许开发者将大规模的数据集分割成小块，并在多个计算单元上同时处理，以提高计算效率。 3. **内存模型**：OpenCL具有多种内存区域，如全局内存、局部内存、私有内存和常量内存，理解它们的特性和使用方式对于优化性能至关重要。 4. **内核(Kernel)**：OpenCL程序的核心是内核，这是一种并行执行的函数，可以在设备上运行。开发者需要编写内核代码，定义如何处理数据。 5. **同步与通信**：OpenCL提供了同步机制来协调不同计算单元间的操作，同时，数据在主机和设备间传输的管理也是编程中的重要环节。 6. **编译与链接**：OpenCL内核是在运行时动态编译的，这使得程序可以适应不同的硬件环境，但同时也需要理解编译和链接的过程。 7. **错误处理**：OpenCL API提供了丰富的错误检查机制，理解和正确处理错误是保证程序稳定性的关键。 8. **性能调优**：优化OpenCL程序涉及内存访问模式、计算效率、减少数据传输等多方面，开发者需要深入理解设备特性以达到最佳性能。 9. **工具与库**：OpenCL生态系统包含许多工具和库，如oclGrind（OpenCL调试器）、Intel OpenCL SDK（软件开发工具包）等，这些工具帮助开发者更轻松地编写和测试OpenCL程序。通过《OpenCL Programming Guide》，读者可以学习到如何利用OpenCL创建并行计算应用程序，充分发挥硬件的潜能，实现高性能计算任务。该书可能还会包含实际案例、示例代码和实践指导，以帮助读者将理论知识转化为实际应用。

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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