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).

并发性提供了一种构建解决方案的方法，以解决可能(但不一定)可并行的问题， 并发性是关于结构，并行性是关于执行。

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).

并发性和并行性源

在单个处理器上的多线程进程中，处理器可以在线程之间切换执行资源，从而实现并发执行。

在共享内存多处理器环境中的同一个多线程进程中，进程中的每个线程可以同时在单独的处理器上运行，从而导致并行执行。

当进程的线程数量与处理器数量相同或较少时，线程支持系统结合操作环境确保每个线程运行在不同的处理器上。

例如，在具有相同数量的线程和处理器的矩阵乘法中，每个线程(和每个处理器)计算结果的一行。

并发:在单核机器上，多任务以cpu时间片共享的方式运行。 并行:在多核机器上，多个任务同时在每个核上运行。

我在一些博客上找到了这个内容。认为它是有用的和相关的。

并发性和并行性不是一回事。两个任务T1和T2是并发的，如果这两个任务的执行顺序不是预先确定的，

T1可以在T2之前执行和完成， T2可以在T1之前执行和完成， T1和T2可以在同一个时间实例中同时执行(并行性)， T1和T2可以交替执行， .．. 如果操作系统安排两个并发线程在一个单核非smt非cmp处理器上运行，您可能会得到并发性而不是并行性。并行在多核、多处理器或分布式系统上是可能的。

并发性通常被认为是程序的一种属性，是一个比并行性更普遍的概念。

来源:https://blogs.oracle.com/yuanlin/entry/concurrency_vs_parallelism_concurrent_programming