CUDA编程最佳实践手册：优化CUDA架构性能

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"CUDA_C_Best_Practices_Guide"是一份详尽的手册，旨在帮助NVIDIA CUDA架构的开发者实现最高性能优化。该指南着重于介绍和实践CUDA编程的最佳策略，通过阐述优化技巧、编程模式以及简化CUDA架构下的编程。它以"评估、并行化、优化、部署"（Assess, Parallelize, Optimize, Deploy）的核心流程进行组织。首先，手册涵盖了异构计算的基本概念，区分了主机（CPU）与设备（GPU）之间的差异，并解释了在CUDA支持的设备上运行的软件特性。第2章深入到应用性能分析，教授如何创建性能剖析，识别热点代码区域，理解强扩展性和Amdahl定律，以及弱扩展性和Gustafson定律，这些理论有助于开发者确定何时及如何利用这两种不同的扩展性原则。第3章引导读者入门CUDA编程，涉及并行库的使用、如何让编译器支持并行计算，以及如何设计代码以展示并行性。这章对初学者至关重要，确保他们了解如何有效地将任务分解为可并行执行的部分。第4章关注验证和正确性，强调了程序验证的重要性，包括代码的复核和使用单元测试来确保结果的准确性。这对于保证高性能计算的正确性和可靠性至关重要。此外，手册还提供了推荐和最佳实践，包括内存管理和优化策略，以及如何在实际部署时考虑到硬件资源、性能瓶颈和能耗等因素。随着版本的更新，从4.1版到5.0版，指南可能已经根据用户的反馈和CUDA架构的最新进展进行了重新排列和内容优化。这份"CUDA_C_Best_Practices_Guide"是一本实用的参考书，对于希望充分利用NVIDIA GPU资源，提高CUDA应用程序性能的开发人员来说，它是一份不可或缺的工具。通过遵循指南中的建议，开发者可以避免常见陷阱，提升代码效率，从而实现高效、稳定的CUDA编程。

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CUDA C Best Practices Guide DG-05603-001_v5.0|1

Chapter 1.

HETEROGENEOUS COMPUTING

CUDA programming involves running code on two different platforms concurrently: a

host system with of one or more CPUs and one or more CUDA-enabled NVIDIA GPU

devices.

While NVIDIA GPUs are frequently associated with graphics, they are also powerful

arithmetic engines capable of running thousands of lightweight threads in parallel. This

capability makes them well suited to computations that can leverage parallel execution.

However, the device is based on a distinctly different design from the host system, and

it's important to understand those differences and how they determine the performance

of CUDA applications in order to use CUDA effectively.

1.1Differences between Host and Device

The primary differences are in threading model and in separate physical memories:

Threading resources

Execution pipelines on host systems can support a limited number of concurrent

threads. Servers that have four hex-core processors today can run only 24 threads

concurrently (or 48 if the CPUs support Hyper¬Threading.) By comparison, the

smallest executable unit of parallelism on a CUDA device comprises 32 threads

(termed a warp of threads). Modern NVIDIA GPUs can support up to 1536 active

threads concurrently per multiprocessor (see Features and Specificationsof the CUDA C

Programming Guide) On GPUs with 16 multiprocessors, this leads to more than 24,000

concurrently active threads.

Threads

Threads on a CPU are generally heavyweight entities. The operating system

must swap threads on and off CPU execution channels to provide multithreading

capability. Context switches (when two threads are swapped) are therefore slow and

expensive. By comparison, threads on GPUs are extremely lightweight. In a typical

system, thousands of threads are queued up for work (in warps of 32 threads each).

If the GPU must wait on one warp of threads, it simply begins executing work on

another. Because separate registers are allocated to all active threads, no swapping of

registers or other state need occur when switching among GPU threads. Resources

stay allocated to each thread until it completes its execution. In short, CPU cores are

Heterogeneous Computing

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CUDA C Best Practices Guide DG-05603-001_v5.0|2

designed to minimize latency for one or two threads at a time each, whereas GPUs

are designed to handle a large number of concurrent, lightweight threads in order to

maximize throughput.

RAM

The host system and the device each have their own distinct attached physical

memories. As the host and device memories are separated by the PCI Express (PCIe)

bus, items in the host memory must occasionally be communicated across the bus

to the device memory or vice versa as described in What Runs on a CUDA-Enabled

Device?

These are the primary hardware differences between CPU hosts and GPU devices with

respect to parallel programming. Other differences are discussed as they arise elsewhere

in this document. Applications composed with these differences in mind can treat the

host and device together as a cohesive heterogeneous system wherein each processing

unit is leveraged to do the kind of work it does best: sequential work on the host and

parallel work on the device.

1.2What Runs on a CUDA-Enabled Device?

The following issues should be considered when determining what parts of an

application to run on the device:

‣

The device is ideally suited for computations that can be run on numerous data

elements simultaneously in parallel. This typically involves arithmetic on large

data sets (such as matrices) where the same operation can be performed across

thousands, if not millions, of elements at the same time. This is a requirement for

good performance on CUDA: the software must use a large number (generally

thousands or tens of thousands) of concurrent threads. The support for running

numerous threads in parallel derives from CUDA's use of a lightweight threading

model described above.

‣

For best performance, there should be some coherence in memory access by adjacent

threads running on the device. Certain memory access patterns enable the hardware

to coalesce groups of reads or writes of multiple data items into one operation. Data

that cannot be laid out so as to enable coalescing, or that doesn't have enough locality

to use the L1 or texture caches effectively, will tend to see lesser speedups when used

in computations on CUDA.

‣

To use CUDA, data values must be transferred from the host to the device along

the PCI Express (PCIe) bus. These transfers are costly in terms of performance and

should be minimized. (See Data Transfer Between Host and Device.) This cost has

several ramifications:

‣

The complexity of operations should justify the cost of moving data to and from

the device. Code that transfers data for brief use by a small number of threads

will see little or no performance benefit. The ideal scenario is one in which many

threads perform a substantial amount of work.

For example, transferring two matrices to the device to perform a matrix

addition and then transferring the results back to the host will not realize much

performance benefit. The issue here is the number of operations performed per

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CUDA编程最佳实践手册：优化CUDA架构性能

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