OpenCL编程指南：CUDA架构与性能优化

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"OpenCL Programming Guide (NVIDIA)" 是一本针对使用OpenCL在NVIDIA CUDA架构上进行编程的指南，涵盖了从基础概念到性能优化的各种主题。本书旨在帮助开发者理解和利用OpenCL这一开放标准，实现CPU与GPU之间的数据通信，并有效地利用不同类型的内存以达到最小的消耗。 OpenCL，全称为Open Computing Language，是一种跨平台的并行计算框架，主要用于硬件加速计算，特别是在图形处理单元（GPU）上执行通用计算任务。CUDA（Compute Unified Device Architecture）是NVIDIA为GPU开发的一种编程模型，使得开发者能够利用GPU的强大计算能力执行非图形相关的计算任务。在介绍CUDA架构时，书中的章节讲述了以下几点： 1. SIMT（Single Instruction Multiple Thread）架构是CUDA的基础，它允许单个GPU核心执行多个线程，这些线程在同一指令下并行操作，提高了计算效率。 2. 硬件多线程特性使得GPU能在单个流处理器（Streaming Multiprocessor, SM）上并发运行多个线程块，进一步提升并行度。 3. PTX（Parallel Thread Execution）是CUDA的中间代码表示，它是编译CUDA程序时的一个阶段，用于生成可被执行的GPU指令。 4. Volatile关键字用于标记变量，表明其状态可能在GPU的不同线程之间发生变化，需要特殊处理。 5. 计算能力（Compute Capability）是NVIDIA GPU的一个指标，它定义了设备能支持的特性级别和性能。书中还提到了性能指导原则，如： 1. 整体性能优化策略，这涉及到从应用程序设计层面到硬件资源使用的全局考虑。 2. 提高利用率是优化的关键，包括应用层面、设备层面和多处理器层面的优化，确保GPU资源得到充分使用。 3. 提升内存带宽是性能优化的另一个重要方面，涉及如何高效地传输数据以及访问不同类型的设备内存： - 主机与设备间的数据传输需要精心设计以减少延迟和提高吞吐量。 - 全局内存是所有线程可访问的存储，但访问速度较慢，优化时要考虑减少全局内存访问。 - 局部内存（Local Memory）是线程块内部共享的存储，对于局部数据的快速交换非常有用。 - 共享内存（Shared Memory）在同一个SM上的线程之间快速共享，可显著提升数据交换速度。 - 常量内存（Constant Memory）提供了一种高速且只读的存储方式，适合存储不会改变的常量数据。通过深入理解OpenCL和CUDA的编程模型，开发者可以编写出高效利用GPU计算能力的程序，实现高性能计算任务，例如矩阵乘法这样的并行计算示例，书中的实例将帮助读者更好地掌握这些技术。

Chapter 2.

OpenCL on the CUDA Architecture

2.1 CUDA Architecture

The CUDA architecture is a close match to the OpenCL architecture.

A CUDA device is built around a scalable array of multithreaded Streaming

Multiprocessors (SMs). A multiprocessor corresponds to an OpenCL compute unit.

A multiprocessor executes a CUDA thread for each OpenCL work-item and a thread

block for each OpenCL work-group. A kernel is executed over an OpenCL

NDRange by a grid of thread blocks. As illustrated in Figure 2-1, each of the thread

blocks that execute a kernel is therefore uniquely identified by its work-group ID,

and each thread by its global ID or by a combination of its local ID and work-group

ID.

Chapter 1

OpenCL Programming Guide Version 3.2 13

maximize utilization of its functional units, it leverages thread-level parallelism by

using hardware multithreading as detailed in Section 2.1.2, more so than instruction-

level parallelism within a single thread (instructions are pipelined, but unlike CPU

cores they are executed in order and there is no branch prediction and no

speculative execution).

Sections 2.1.1 and 2.1.2 describe the architecture features of the streaming

multiprocessor that are common to all devices. Sections C.3.1 and C.4.1 provide the

specifics for devices of compute capabilities 1.x and 2.0, respectively (see Section 2.3

for the definition of compute capability).

2.1.1 SIMT Architecture

The multiprocessor creates, manages, schedules, and executes threads in groups of

32 parallel threads called warps. Individual threads composing a warp start together

at the same program address, but they have their own instruction address counter

and register state and are therefore free to branch and execute independently. The

term warp originates from weaving, the first parallel thread technology. A half-warp is

either the first or second half of a warp. A quarter-warp is either the first, second,

third, or fourth quarter of a warp.

When a multiprocessor is given one or more thread blocks to execute, it partitions

them into warps that get scheduled by a warp scheduler for execution. The way a block

is partitioned into warps is always the same; each warp contains threads of

consecutive, increasing thread IDs with the first warp containing thread 0.

Section 2.1 describes how thread IDs relate to thread indices in the block.

A warp executes one common instruction at a time, so full efficiency is realized

when all 32 threads of a warp agree on their execution path. If threads of a warp

diverge via a data-dependent conditional branch, the warp serially executes each

branch path taken, disabling threads that are not on that path, and when all paths

complete, the threads converge back to the same execution path. Branch divergence

occurs only within a warp; different warps execute independently regardless of

whether they are executing common or disjoint code paths.

The SIMT architecture is akin to SIMD (Single Instruction, Multiple Data) vector

organizations in that a single instruction controls multiple processing elements. A

key difference is that SIMD vector organizations expose the SIMD width to the

software, whereas SIMT instructions specify the execution and branching behavior

of a single thread. In contrast with SIMD vector machines, SIMT enables

programmers to write thread-level parallel code for independent, scalar threads, as

well as data-parallel code for coordinated threads. For the purposes of correctness,

the programmer can essentially ignore the SIMT behavior; however, substantial

performance improvements can be realized by taking care that the code seldom

requires threads in a warp to diverge. In practice, this is analogous to the role of

cache lines in traditional code: Cache line size can be safely ignored when designing

for correctness but must be considered in the code structure when designing for

peak performance. Vector architectures, on the other hand, require the software to

coalesce loads into vectors and manage divergence manually.

If a non-atomic instruction executed by a warp writes to the same location in global

or shared memory for more than one of the threads of the warp, the number of

serialized writes that occur to that location varies depending on the compute

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