Concurrency Concurrency means that an application is making progress on more than one task at the same time (concurrently). Well, if the computer only has one CPU the application may not make progress on more than one task at exactly the same time, but more than one task is being processed at a time inside the application. It does not completely finish one task before it begins the next. Parallelism Parallelism means that an application splits its tasks up into smaller subtasks which can be processed in parallel, for instance on multiple CPUs at the exact same time. Concurrency vs. Parallelism In Detail As you can see, concurrency is related to how an application handles multiple tasks it works on. An application may process one task at at time (sequentially) or work on multiple tasks at the same time (concurrently). Parallelism on the other hand, is related to how an application handles each individual task. An application may process the task serially from start to end, or split the task up into subtasks which can be completed in parallel. As you can see, an application can be concurrent, but not parallel. This means that it processes more than one task at the same time, but the tasks are not broken down into subtasks. An application can also be parallel but not concurrent. This means that the application only works on one task at a time, and this task is broken down into subtasks which can be processed in parallel. Additionally, an application can be neither concurrent nor parallel. This means that it works on only one task at a time, and the task is never broken down into subtasks for parallel execution. Finally, an application can also be both concurrent and parallel, in that it both works on multiple tasks at the same time, and also breaks each task down into subtasks for parallel execution. However, some of the benefits of concurrency and parallelism may be lost in this scenario, as the CPUs in the computer are already kept reasonably busy with either concurrency or parallelism alone. Combining it may lead to only a small performance gain or even performance loss. Make sure you analyze and measure before you adopt a concurrent parallel model blindly.

从http://tutorials.jenkov.com/java-concurrency/concurrency-vs-parallelism.html

并发和并行实际上是相同的原理，两者都与同时执行的任务有关，尽管我想说并行任务应该是真正的多任务，“同时”执行，而并发可能意味着任务共享执行线程，同时仍然看起来是并行执行。

异步方法与前两个概念没有直接关系，异步被用来呈现并发或并行任务的印象，但实际上异步方法调用通常用于需要在当前应用程序之外执行工作的进程，我们不希望等待和阻塞应用程序等待响应。

例如，从数据库获取数据可能需要时间，但我们不想阻塞UI等待数据。异步调用接受回调引用，并在请求被放置到远程系统后立即将执行返回给您的代码。当远程系统执行所需的任何处理时，UI可以继续响应用户，一旦它将数据返回给回调方法，那么该方法就可以适当地更新UI(或移交更新)。

从用户的角度来看，它看起来像多任务处理，但它可能不是。

EDIT

可能值得补充的是，在许多实现中，异步方法调用会导致线程启动，但这不是必要的，这实际上取决于正在执行的操作以及如何将响应通知回系统。

我用真实的场景来解释3个话题 假设你想去艾哈迈达巴德到孟买旅行，但你不知道怎么走，所以你决定使用地图应用程序(谷歌Maps)。

很正常但效率很低的一种方法是，你可以在开车前看完整的路径，然后你开始开车并到达目的地。

平行-你可以不断地驾驶和观察路径。 异步-你的朋友和你在车里，你给他你的手机打开地图应用程序，告诉他看地图和指导你。 同时行驶——你开了几公里，把车停在一边，看地图，找方向，然后继续开车。

我会让它简短而有趣，让你们能够理解这些概念。

并发与并行——任务执行的方式。

Take an example in real life: There’s a challenge that requires you to both eat a whole huge cake and sing a whole song. You’ll win if you’re the fastest who sings the whole song and finishes the cake. So the rule is that you sing and eat concurrently. How you do that does not belong to the rule. You can eat the whole cake, then sing the whole song, or you can eat half a cake, then sing half a song, then do that again, etc. Parallelism is a specific kind of concurrency where tasks are really executed simultaneously. In computer science, parallelism can only be achieved in multicore environments.

同步与异步——编程模型。

在同步中，您将代码编写为按顺序从上到下执行的步骤 底部。在异步编程模型中，你将代码作为任务编写， 然后并发执行。并发执行意味着 所有的任务都可能同时执行。

并发+并行都意味着同时运行多个任务。我看到一些人认为这是有区别的，但这取决于你咨询的参考文献，没有真正的正确或错误的答案。

Asynchronous: In some communities this means non-blocking code, which can mean two things: It almost always means it will not block an OS thread. However, non-blocking can optionally mean that the next line of source code in a function will continue to run without delay. In Python await asyncio.sleep(5) blocks execution of the function, but not the OS thread, and that is considered async. In Golang, you have "goroutines" that similarly to Python's await, they block execution of code, but not OS threads, however, this is not referred to as async in the Golang community. It's just concurrent programming.

当一个经理有几个员工，并且可以给他们每个人一个单独的任务时，并行性就会发生。工人们做他们的工作，并向经理提供结果。如果任务不能完全分离，例如任务之间的结果有一定的依赖性，或者需要在没有其他推理的情况下使用相同的资源，那么并行度就受到这些约束，不能完全实现。

Concurrency happens when a manager has several tasks but only less workers, hence some workers are given more than one task. Any worker given multiple tasks, divides each original given task to several steps and does the steps interleaved, each task result will be given back to manager as soon as every steps of it finished. Manager receive a task result while other tasks started and progressed several steps but have not finished yet. If any worker with multiple task decides not to start a single step of a given task before finishing every steps of an already started task, this is called sequentiality.

Asynchrony is any of the two above mixed or separated, seen from the manager's point of view. When the manager assigns the tasks to either few or enough workers he shall not be awaited stalled until any results are given back. He can do his personal jobs or whatever, while jobs are progressing. Usually workers do not decide how tasks should be divided into steps. An inversion of control means manager decides over steps and gives single steps to workers. So when he receives a step result from a worker, give him another step, maybe from another task. The whom under control is responsible for composing back step results into task results as well. So Asynchronicity comes with responsibility for control and probably coordination. If any worker is urged to work sequentially, from manager's point of view he is a synchronous worker.

Summary As it's simple to guess, full parallelism is an unrealisable idea unless otherwise in rare mostly trivial cases. Since reality comes with interdependent tasks and shared resources and lack of workers. So concurrency is the reality. From manager's point of view this concurrency is best if it does not hinder him from fine controlling the tasks, and if positive it is called asynchronous. Also computer software engineering best practices, augmented by S in SOLID principle, historically made servers single step runners called micro-services, this returned back control to the clients. So current situation is concurrency from server point of view and asynchronicity from client point of view.