Intel多线程开发指南：内存管理和编程工具

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"Intel多线程程序开发指南2" Intel的《多线程程序开发指南》第二部分主要聚焦在内存管理和编程工具上，是为在Intel架构的对称多处理器（SMP）系统以及支持Intel超线程技术的系统上开发高效多线程应用的开发者提供的一份参考资料。该指南旨在提升多线程性能，减少在当前和未来SMP架构中可能出现的性能波动。第二部分的内容包括了一系列的技术论文，向软件开发者提供了最新的内存管理策略和有助于加速开发、简化并行编程的工具信息。这部分是第一部分的延伸，第一部分主要涉及多线程应用程序的线程同步技术。每个主题都独立讨论了重要的线程问题，如内存访问效率、数据一致性、锁的使用和优化、内存模型等，并在整个指南中通过交叉引用链接到相关的深入知识，使得开发者能够轻松获取更多信息。其中，内存管理部分可能涵盖了以下几个关键知识点： 1. **内存模型**：介绍Intel处理器的内存模型，包括缓存层次结构、内存一致性模型，以及它们如何影响多线程程序的行为。 2. **线程局部存储**：讲解如何使用线程局部存储（TLS）来减少共享数据的冲突，提高性能。 3. **内存分配策略**：探讨最佳的内存分配实践，比如堆和栈的使用、内存池以及动态内存管理库的优化。 4. **数据对齐**：强调正确数据对齐的重要性，以避免潜在的性能问题和数据损坏。 5. **并发数据结构**：介绍设计和使用并发容器和数据结构，如线程安全的队列、栈和哈希表。 6. **内存屏障**：解释内存屏障在确保内存操作顺序和可见性中的作用。 7. **锁和同步**：讨论各种锁机制（如自旋锁、互斥量、读写锁等）的优缺点，以及如何有效地使用它们来避免竞态条件。编程工具部分可能包含： 1. **性能分析工具**：介绍如何使用如Intel VTune Amplifier等工具来识别和解决性能瓶颈。 2. **调试工具**：讨论多线程程序的调试技巧，如使用线程可视化工具进行问题定位。 3. **并行编译器选项**：说明如何利用编译器优化多线程代码，例如OpenMP的使用。 4. **性能调优指南**：提供针对特定平台的性能调优建议和最佳实践。此指南通过实例和案例研究，帮助开发者理解这些概念，并提供实用的建议来提高代码的可扩展性和性能。对于想要深入理解Intel平台多线程编程的开发者来说，这是一份极具价值的资源。

Intel Guide for Developing Multithreaded Applications | Memory Management

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In OpenMP, one can also create thread-local variables by specifying them in a private clause on the parallel pragma. These

variables are automatically deallocated at the end of the parallel region. Of course, another way to specify thread-local data,

regardless of the threading model, is to use variables allocated on the stack in a given scope. Such variables are deallocated at

the end of the scope.

Advice

The technique of thread-local storage is applicable if synchronization is coded within a time-critical section of code, and if the

operations being synchronized need not be ordered in real-time. If the real-time order of the operations is important, then the

technique can still be applied if enough information can be captured during the time-critical section to reproduce the ordering

later, during a non-time-critical section of code.

Consider, for example, the following example, where threads write data into a shared buffer:

Assume that time is some monotonically increasing value and the only real requirement of the program for this buffer data is

that it be written to a le occasionally, sorted according to time. Synchronization can be eliminated in the update_log routine

by using thread-local buffers. Each thread would allocate a separate copy of tpbuer and tpcurrent_ptr. This allows the

elimination of the critical section in update_log. The entries from the various thread-private buffers can be merged later,

according to the time values, in a non-time-critical portion of the program.

Intel Guide for Developing Multithreaded Applications | Memory Management

*Other names and brands may be claimed as the property of others.

3.3 – Detecting Memory Bandwidth Saturation in Threaded Applications

Abstract

Memory sub-system components contribute signicantly to the performance characteristics of an application. As an increas-

ing number of threads or processes share the limited resources of cache capacity and memory bandwidth, the scalability of

a threaded application can become constrained. Memory-intensive threaded applications can suffer from memory bandwidth

saturation as more threads are introduced. In such cases, the threaded application won’t scale as expected, and performance

can be reduced. This article introduces techniques to detect memory bandwidth saturation in threaded applications.

This article is part of the larger series, “Intel Guide for Developing Multithreaded Applications,” which provides guidelines for

developing efcient multithreaded applications for Intel® platforms.

Background

As modern processors include more cores and bigger caches, they become faster at a higher rate than the memory sub-sys-

tem components. The increasing core count on a per-die basis has put pressure on the cache capacity and memory bandwidth.

As a result, optimally utilizing the available cache and memory bandwidth to each core is essential in developing forward-

scaling applications. If a system isn’t capable of moving data from main memory to the cores fast enough, the cores will sit idle

as they wait for the data to arrive. An idle core during computation is a wasted resource that increases the overall execution

time of the computation and will negate some of the benets of more cores.

The current generation of Intel® processors based on the Nehalem architecture moved from the traditional front-side-bus

(FSB) approach to non-uniform memory access/architecture (NUMA) model to increase the available memory bandwidth to the

cores and reduce the bandwidth saturation issues mentioned above. Figure 1 depicts the FSB to NUMA transition.

Figure 1. Transition from FSB to NUMA.

The clear symptom of bandwidth saturation for any parallel application is non-scaling behavior. In other words, an application

that has saturated the available memory bandwidth will not scale effectively to more threads or cores. However, there are

many causes for multi-threaded applications not to scale and some of these performance inhibiting factors include threading

overhead, synchronization overhead, load imbalance, and inappropriate granularity. Intel® Thread Proler is designed to iden-

tify such performance issues at the application level.

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