您今天推荐什么并行编程模型来利用明天的众核处理器? [英] What parallel programming model do you recommend today to take advantage of the manycore processors of tomorrow?

问题描述

如果您今天从头开始编写一个新应用程序，并希望它扩展到明天可以投入的所有内核，您会选择哪种并行编程模型/系统/语言/库?为什么?

If you were writing a new application from scratch today, and wanted it to scale to all the cores you could throw at it tomorrow, what parallel programming model/system/language/library would you choose? Why?

我对这些轴上的答案特别感兴趣:

I am particularly interested in answers along these axes:

1. 程序员生产力/易用性(凡人能成功使用吗?)
2. 目标应用领域(它(不)擅长什么问题?)
3. 并发风格(它是否支持任务、管道、数据并行、消息...?)
4. 可维护性/面向未来(20 年后还会有人使用它吗?)
5. 性能(它如何在何种硬件上扩展?)

我故意对应用程序的性质含糊其辞，以期获得对各种应用程序有用的良好的通用答案.

I am being deliberately vague on the nature of the application in anticipation of getting good general answers useful for a variety of applications.

推荐答案

多核编程实际上可能需要不止一种范式.目前的一些竞争者是:

Multi-core programming may actually require more than one paradigm. Some current contenders are:

1. MapReduce.这适用于可以轻松将问题分解为并行块的问题.
2. 嵌套数据并行.这类似于 MapReduce，但实际上支持问题的递归分解，即使递归块的大小不规则.期待 NDP 在运行在大规模并行但有限的硬件(如 GPU)上的纯函数式语言中取得巨大成功.
3. 软件事务内存.如果您需要传统线程，STM 让它们变得可以忍受.您在关键部分付出了 50% 的性能损失，但您可以轻松地将复杂的锁定方案扩展到数百个处理器.但是，这不适用于分布式系统.
4. 具有消息传递的并行对象线程.Erlang 使用了这个非常聪明的模型.每个对象"成为一个轻量级线程，对象通过异步消息和模式匹配进行通信.它基本上是真正的并行 OO.这在几个现实世界的应用程序中取得了很好的成功，并且非常适用于不可靠的分布式系统.

1. MapReduce. This works well where a problem can be easily decomposed into parallel chunks.
2. Nested Data Parallelism. This is similar to MapReduce, but actually supports recursive decomposition of a problem, even when the recursive chunks are of irregular size. Look for NDP to be a big win in purely functional languages running on massively parallel but limited hardware (like GPUs).
3. Software Transactional Memory. If you need traditional threads, STM makes them bearable. You pay a 50% performance hit in critical sections, but you can scale complex locking schemes to 100s of processors without pain. This will not, however, work for distributed systems.
4. Parallel object threads with messaging. This really clever model is used by Erlang. Each "object" becomes a lightweight thread, and objects communicate by asynchronous messages and pattern matching. It's basically true parallel OO. This has succeeded nicely in several real-world applications, and it works great for unreliable distributed systems.

其中一些范例可以为您提供最高性能，但只有在问题完全分解时才有效.其他人牺牲了一些性能，但允许更广泛的算法.我怀疑上述的某种组合最终会成为一个标准的工具包.

Some of these paradigms give you maximum performance, but only work if the problem decomposes cleanly. Others sacrifice some performance, but allow a wider variety of algorithms. I suspect that some combination of the above will ultimately become a standard toolkit.

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