Intel并行计算与MPI通信优化

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"本文主要介绍了平行计算的概念，特别是在Intel C++ Composer XE for Windows环境中的应用。文中提到了OpenMP 4.0标准，这是并行编程的一个重要框架，允许开发者利用多核处理器的潜力来加速计算密集型任务。此外，文章还提及了Intel Cluster Studio XE 2013 SP1，这是一个高性能计算（HPC）软件开发工具套件，包含了Intel MPI Library和Intel Trace Analyzer and Collector等关键组件，用于优化分布式内存程序的性能。 Intel Cluster Studio XE 2013 SP1的引入是本文的核心部分，它带来了对MPI（Message Passing Interface）库的最新改进，使得跨节点通信更加高效。James Tullos在文章中强调了Intel Trace Analyzer and Collector的重要性，这是一款性能分析工具，能够帮助开发者识别并行应用程序中的性能瓶颈，从而提升MPI应用的性能。文章还提及了一个名为Pexip的公司，他们使用Intel Parallel Studio XE来加速视频会议技术，展示了并行计算在实际应用中的价值。Pexip的例子说明了通过优化并行处理能力，可以显著改善实时通信服务的性能，提供更流畅的用户体验。并行计算的实践涉及到多线程编程、负载均衡、数据同步等多个方面。OpenMP作为一种广泛使用的并行编程接口，提供了共享内存模型下的并行化支持，包括任务并行和循环并行。而MPI则是在分布式内存系统中实现进程间通信的标准，适用于大规模并行计算和集群环境。通过Intel的工具集，开发者可以利用OpenMP和MPI的优势，结合Intel的硬件优化，实现高效、可靠的并行程序设计。这不仅可以提升单个计算任务的速度，还可以在大规模数据处理和科学计算等领域带来指数级的性能提升。" 总结来说，这篇文章讨论了并行计算的关键技术和工具，尤其是Intel提供的解决方案，这些技术对于提升现代计算系统的性能和效率至关重要。OpenMP 4.0和Intel MPI Library的结合使用，以及Intel Cluster Studio XE 2013 SP1中的性能分析工具，为开发者提供了强大的手段，以便在Windows平台上实现高效的并行计算应用。

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because it creates a load imbalance, the schedule clause can change the default distribution scheme. For

instance, schedule(dynamic, c) creates chunks of size c, and idle threads grab the next available chunk.

OpenMP also provides mechanisms to describe the visibility of data to the threads (“scoping“). It is important

to tell the OpenMP compiler what data must remain in the shared memory domain and what data needs to be

private to the individual threads. OpenMP denes clauses that can be added to the directives to control the scope

of variables. The shared clause keeps a variable in the shared space, while the private clause creates a

thread-private copy of a variable. Shared scope is the default for variables that are declared outside of a parallel

region. In Figure 1, this applies to the variables A, B, C, and the constant N. Private variables can store

different values for different threads, as needed by the loop counter i, in the example. Private copies are by

default created without initializing; rstprivate can be used to assign the value of the variable outside

of the parallel region.

The latest version 4.0 of the OpenMP API specication not only includes minor bug xes and improvements

to existing features, it now supports a good share of features introduced with Fortran 2003. OpenMP afnity

denes a common way to express thread afnity to execution units of the hardware. Version 4.0 also comes

with major feature enhancements, some of which will be discussed in more detail here. Task groups improve

tasking by providing a better way to express synchronization of a set of tasks and to handle cancellation,

which allows to stop parallel execution. SIMD pragmas extend the thread-parallel execution to data-parallel

SIMD machine instructions, while user-dened reductions let programmers specify arbitrary reduction operations.

Possibly the biggest addition to OpenMP is support for ofoading computation to coprocessor devices.

Talk the Talk, Task the Task

The growing number of cores (and threads) make it harder to fully utilize the cores with traditional worksharing

constructs for parallel loops. Irregular algorithms, such as recursions and traversals of graphs, require a completely

different approach to parallelism. Task-based models blend well with the requirements of these algorithms,

since tasks can be created in a much more exible way.

An OpenMP task may be treated as a small package that consists of a piece of code to be executed and all the

data needed for execution. An OpenMP task is created through the #pragma omp task directive to mark

a piece of code and data for concurrent execution. The OpenMP runtime system takes care of mapping the

created tasks to the threads of a parallel region. It may defer the task for later execution by adding it to a task

queue or it may execute the task immediately.

Figure 2 shows a task-parallel version of a very simple, brute-force Sudoku* solver. The idea of the algorithm is:

1. Find an empty eld without a number

2. Insert a number

3. Check the Sudoku board

4. If the solution is invalid, try the next possible number

5. If the solution is valid, go to the next eld and start over

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For more information regarding performance and optimization choices in Intel

software products,

visit http://software.intel.com/en-us/articles/optimization-notice.

8The Parallel Universe

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