OpenCL编程指南：GPU并行计算入门

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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，属于OpenGL系列图书。本书旨在帮助程序员理解和掌握OpenCL标准，并发掘其全部潜力。OpenCL是一种开放标准，用于并行计算，允许程序员利用各种硬件平台（如CPU和GPU）的计算能力。" OpenCL，全称Open Computing Language，是跨平台的API，设计用于分布式计算，特别是针对异构计算系统。它提供了一个框架，使得开发者可以编写程序，这些程序能够在多种不同类型的处理器上运行，包括CPU、GPU、FPGA以及DSP等。OpenCL的目标是实现高效能计算，尤其是在处理大规模数据并行任务时。 OpenCL编程涉及到以下几个核心概念： 1. **设备（Devices）**：OpenCL中的设备是指执行计算任务的硬件单元，例如GPU、CPU或其他定制的加速器。开发者可以依据性能需求选择合适的设备来执行特定的计算任务。 2. **上下文（Contexts）**：上下文是设备集合，它定义了程序运行的环境。在上下文中，可以创建队列、缓冲区和其他对象。 3. **命令队列（Command Queues）**：命令队列用于调度任务到设备执行，是提交计算任务和数据传输的地方。 4. **程序（Programs）**：OpenCL程序由一个或多个源代码文件组成，包含内核函数，这些函数在设备上执行。 5. **内核（Kernels）**：内核是OpenCL程序的核心，是可并行执行的函数，可以在设备上并行运行多个实例。 6. **工作项（Work Items）**：每个内核执行的独立实例称为工作项。工作项在设备上并行运行，可以构成工作组。 7. **工作组（Work Groups）**：一组在同一个计算单元上并行运行的工作项，它们之间的通信效率相对较高。 8. **内存模型（Memory Model）**：OpenCL提供了多种内存区域，如全局内存、局部内存、私有内存等，用于优化数据访问速度和设备间的通信。 9. **主机-设备交互（Host-Device Interaction）**：OpenCL提供了API，允许主机程序与设备进行数据交换，如创建缓冲区并将其映射到设备，或者从设备读取结果。通过OpenCL，开发者能够编写高度优化的代码，充分利用硬件资源，从而在科学计算、图像处理、机器学习等领域实现高性能计算。OpenCL的灵活性使得开发人员能够根据特定应用场景选择最佳的计算平台，而无需关注底层硬件细节。结合OpenGL，开发者还能构建高效的图形和计算混合应用程序，将图形渲染与并行计算结合起来，实现更强大的功能。

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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OpenCL编程指南：GPU并行计算入门

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