Integraing data parallel and task parallel can solve these problems.
数据并行与任务并行的结合可以很好地解决这些问题。
The Task Parallel Library, or TPL, is essentially an advanced version of the.net thread pool.
任务并行库，又被称为TPL，本质上是一个高级版本的。NET线程池。
While there are already tools available that enable parallel processing, these tools are largely dedicated to task parallel models.
虽然已经有工具可以使并行处理，这些工具主要是致力于任务并行模式。
A managed programming model for data parallelism, task parallelism, and coordination on parallel hardware unified by a common work scheduler.
一个托管编程模型，支持数据并行处理，任务并行处理，并通过一个通用的工作调度器统一协调并行运行的硬件。
Firstly, this paper introduces two existed parallel execution models: SPMD and MPMD supporting data parallel and task parallel languages, respectively.
本文首先介绍两种典型的并行执行方式：支持数据并行语言的SPMD方式和支持任务并行语言的MPMD方式。
Just like code that targets the Task Parallel Library, PLINQ queries scale in the degree of concurrency based on the capabilities of the host computer.
就像面向任务并行库的代码一样，PLINQ查询会根据主计算机的能力按比例调整并发程度。
The task parallel model is built around the idea that parallelism can be extracted by constructing threads that each have their own goal or task to complete.
任务并行模型是建立在并行想法可以提取的建设，每个线程有自己的目标或完成的任务。
Microsoft has been working on ways to improve the performance of parallel applications in.net 4.5, specifically those using the Task parallel Library. Here is a preview of what you can expect to see.
微软正在努力改进。NET 4.5中应用程序的性能，特别是使用任务并行类库(Task ParallelLibrary)的那些应用。
We emphasized that the RUP has the advantage of allowing task overlaps; that is, different phases can have tasks taking place in parallel.
我们向他们强调了RUP具有允许任务交迭执行的好处；也就是说，不同阶段的任务可以并行的被执行。
As the task size increased, so did the difference in execution time between remote parallel execution and local execution.
随着任务量的增加，远程并行执行和本地执行所需时间的差别也越来越明显。
Instead of setting a parameter in some configuration document, you need to analyze the environment and design the setup so that parallel CSLD task threads are possible.
这不是仅仅通过设置某种配置文档中的一个参数就可以完成的，而是需要对环境进行分析，设计设置，使并行CSLD任务线程成为可能。
This means that there are 5 parallel CSLD task threads.
这意味着有5个并行的CSLD任务线程。
In the content of this article, parallelism is the ability to execute multiple requests in parallel or to split a large dataset task into multiple subtasks which are executed in parallel.
在本文的内容中，并行性指的是能够并行执行多个请求或者将大数据量任务划分成多个并行执行的子任务。
The output of the parallel routing task is created by aggregating the results from the subtasks.
对子任务的结果进行聚合，产生并行路由任务的输出。
What makes grid computing unique is the ability to divide a large task into many smaller ones that can be carried out in parallel.
使网格计算如此独特的是将大型任务拆分为许多可并行执行的较小任务的能力。
It was in such a course that I was introduced to parallel computation at a much finer level of granularity than the task or thread.
在这样的课程中，我在比任务或线程粒度更细的层次上了解了并行计算。
For the first two choices, a single sequential execution path results, while the choice of Task 3 and Task 4 results in both branches executing in parallel.
对于前两个选择，将只导致一条后续执行路径，而同时选择Task3和Task4将导致并行执行这两个分支。
Thus, we explicitly added the situation to the model when Task 3 and Task 4 execute in parallel.
因此，我们在模型中显式添加了Task3和Task4并行执行的情况。
If both branches are triggered, the inclusive decision leads to a parallel execution of Task 2 and Task 3 in the same way as a fork.
如果全部触发这两个分支，则包含性决策将导致Task2和Task3并行执行，其效果与fork类似。
For a single parallel execution of Task 2 and Task 3, we obtain two executions of Task 4.
对于Task2和Task3的单次并行执行，我们将执行Task4两次。
If there is just one CM agent configured, archpro could become a bottleneck since this one agent would need to serialize the archiving operations requested by the four parallel CSLD task threads.
如果只配置了一个CM代理，那么archpro可能成为一个瓶颈，因为这一个代理需要串行地处理由4个并行的CSLD任务线程请求的归档操作。
When starting the CSX task in a command window, the parallel CSX worker and committer threads are listed as depicted in Figure 8.
当在一个命令窗口中开始CSX任务时，并行csxworker线程和committer线程将被列出来，如图8所示。
This is why we need to understand how to increase the number of parallel CSLD task threads so that more CM agents can be used by archpro.
正因为如此，我们需要理解如何增加并行CSLD任务的数量，以便archpro可以使用更多的CM代理。
To optimize the CommonStore throughput, it is necessary to configure the CommonStore task so that it runs with parallel threads.
为了优化CommonStore吞吐量，需要配置 CommonStore任务，使之以并行的线程运行。
This kind of parallel decomposition is often called fork-join because executing a task forks (starts) multiple subtasks and then joins (waits for completion) with them.
这种并行分解方法常常称作fork-join，因为执行一个任务将首先分解（fork）为多个子任务，然后再合并（join）（完成后）。
Revisiting the example from Listing 6, notice that max() is a method already provided on parallel collections, so there is no need to define and invoke a fork/join task directly, as in Listing 7
再看一下清单6中的示例，注意到，max()是一个已经在并行集合中提供的方法，因此没有必要直接定义和调用fork/join任务，如清单 7 所示
MapReduce applications must have the characteristic of "Map" and "Reduce," meaning that the task or job can be divided into smaller pieces to be processed in parallel.
MapReduce应用程序必须具备“映射”和“缩减”的性质，也就是说任务或作业可以分割为小片段以进行并行处理。
To start the parallel task, locate a WorkManager in JNDI, as described above, and hand over the Work object by calling the startWork() method.
要启动并行任务，在JNDI 中查找WorkManager（如上所述）并通过调用startWork()方法移交Work对象。
Thus it is a best practice recommendation to add additional archpro instances (and thus also an additional task) whenever there should be more than ~ 4 parallel "pipes" into the CM repository.
因此，每当有4个以上并行的“管道”通往CM储存库时，建议增加archpro实例(同时也增加任务)。
To route the items in parallel to two process fragments (visualized by the blue boxed areas), the modeler duplicated the outputs of Task 1.
为了将项目并行路由到两个流程片段(即用蓝色框住的区域)，modeler复制了Task 1的输出。
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