从处理器的角度来看，它可以用这张图片来描述

从处理器的角度来看，它可以用这张图片来描述

如果你的程序使用线程(并发编程)，它不一定会这样执行(并行执行)，因为这取决于机器是否可以处理几个线程。

这是一个直观的例子。非线程机器上的线程:

        --  --  --
     /              \
>---- --  --  --  -- ---->>

螺纹机上的螺纹:

     ------
    /      \
>-------------->>

虚线表示执行的代码。正如您所看到的，它们都分开并分别执行，但是线程机器可以同时执行几个单独的部分。

并行编程发生在代码同时被执行并且每次执行都是相互独立的时候。因此，通常不会有关于共享变量之类的关注，因为那不太可能发生。

However, concurrent programming consists on code being executed by different processes/threads that share variables and such, therefore on concurrent programming we must establish some sort of rule to decide which process/thread executes first, we want this so that we can be sure there will be consistency and that we can know with certainty what will happen. If there is no control and all threads compute at the same time and store things on the same variables, how would we know what to expect in the end? Maybe a thread is faster than the other, maybe one of the threads even stopped in the middle of its execution and another continued a different computation with a corrupted (not yet fully computed) variable, the possibilities are endless. It's in these situations that we usually use concurrent programming instead of parallel.

Concurrent programming regards operations that appear to overlap and is primarily concerned with the complexity that arises due to non-deterministic control flow. The quantitative costs associated with concurrent programs are typically both throughput and latency. Concurrent programs are often IO bound but not always, e.g. concurrent garbage collectors are entirely on-CPU. The pedagogical example of a concurrent program is a web crawler. This program initiates requests for web pages and accepts the responses concurrently as the results of the downloads become available, accumulating a set of pages that have already been visited. Control flow is non-deterministic because the responses are not necessarily received in the same order each time the program is run. This characteristic can make it very hard to debug concurrent programs. Some applications are fundamentally concurrent, e.g. web servers must handle client connections concurrently. Erlang, F# asynchronous workflows and Scala's Akka library are perhaps the most promising approaches to highly concurrent programming.

Multicore programming is a special case of parallel programming. Parallel programming concerns operations that are overlapped for the specific goal of improving throughput. The difficulties of concurrent programming are evaded by making control flow deterministic. Typically, programs spawn sets of child tasks that run in parallel and the parent task only continues once every subtask has finished. This makes parallel programs much easier to debug than concurrent programs. The hard part of parallel programming is performance optimization with respect to issues such as granularity and communication. The latter is still an issue in the context of multicores because there is a considerable cost associated with transferring data from one cache to another. Dense matrix-matrix multiply is a pedagogical example of parallel programming and it can be solved efficiently by using Straasen's divide-and-conquer algorithm and attacking the sub-problems in parallel. Cilk is perhaps the most promising approach for high-performance parallel programming on multicores and it has been adopted in both Intel's Threaded Building Blocks and Microsoft's Task Parallel Library (in .NET 4).

虽然没有完整 对并行和并发这两个术语的区别达成一致， 许多作者做了以下区分:

在并发计算中，一个程序可以在任意时刻执行多个任务。 在并行计算中，一个程序是多个任务紧密合作的程序 解决一个问题。

所以并行程序是并发的，但是像多任务操作系统这样的程序也是并发的，即使它运行在一台带有 只有一个核心，因为多个任务可以在任何时刻进行。

来源:Peter Pacheco的《并行编程介绍》

传统的任务调度可以是串行、并行或并发的。

Serial: tasks must be executed one after the other in a known tricked order or it will not work. Easy enough. Parallel: tasks must be executed at the same time or it will not work. Any failure of any of the tasks - functionally or in time - will result in total system failure. All tasks must have a common reliable sense of time. Try to avoid this or we will have tears by tea time. Concurrent: we do not care. We are not careless, though: we have analysed it and it doesn't matter; we can therefore execute any task using any available facility at any time. Happy days.

通常，在已知事件发生时，可用的调度会发生变化，我们称之为状态变化。

人们通常认为这是关于软件的，但实际上这是一种早于计算机的系统设计概念;软件系统的吸收速度有点慢，甚至很少有软件语言试图解决这个问题。如果你感兴趣，你可以试着查一下transputer language occam。

简而言之，系统设计解决以下问题:

动词——你在做什么(操作或算法) 名词——对(数据或接口)进行操作的对象 启动时，进度、状态改变 是串行、并行还是并发 地点——一旦你知道事情发生的时间，你就能说出事情可能发生的地点，而不是之前。 为什么，这是正确的方法吗?还有别的办法吗，更重要的是，更好的办法?如果你不做会怎么样?

祝你好运。