Note: the availability in human tasks parallel to the activity with this name would be unpredictable, since parallel process branches are concurrent.
注意：与具有该名称的活动并行的人工任务可用性将是不可预测的，因为并行流程分支是并发的。
Remote parallel execution was not favorable for smaller tasks because the overhead for communication between the machines was significant.
远程并行执行并不适合小型任务，因为机器间通信的系统开销不容忽视。
There is an additional consideration for parallel paths, because tasks on each path can be performed at the same time; you need to consider how to handle the data.
对于并行路径还存在另外一个注意事项，因为每个路径上的任务可同时执行；您需要考虑如何处理数据。
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。
Increasing the number of parallel CSLD tasks and particularly their internal threads, however, is not as straightforward.
然而，增加并行CSLD任务的数量，特别是增加它们内部的线程数，并不是件容易的事情。
The first two tasks do not depend on each other, which means they are ideal candidates for applying the pattern of serial to parallel work.
前两个任务相互没有依赖性，这意味着它们是应用并行方式执行顺序工作模式的理想对象。
Team work: the structural aspect of architecture allows for the assignment of tasks to teams that can work both in parallel and physically separated from each other.
团队工作：体系结构的结构方面允许为不同的团队(可能是并行地工作，也可能在地理位置上是分开的)分配任务。
This means that remote parallel execution consumed less time on larger tasks.
这就是说，当执行大型任务时，远程并行执行消耗的时间较少。
If the same processing is required for each array element, with no dependencies in the computations, and no communication required between tasks, we have an ideal parallel computing opportunity.
如果每个数组的元素都需要被处理，而且数组间没有依赖关系，执行的计算任务之间也不需要通信，这样的话将是一个执行并行式计算的理想环境。
Sometimes tasks performed sequentially could be done in parallel.
有时按顺序执行的一些任务也可以并行执行。
The MAX DEGREE option on DSNTIP4 determines the maximum degree of parallelism (maximum number of parallel tasks).
DSNTIP4上的MAXDEGREE选项决定了最大并行度（并行任务的最大数量）。
The ideal threshold for choosing between sequential and parallel execution is a function of the cost of coordinating the parallel tasks.
用于选择顺序和并行执行方法的理想阙值是协调并行任务的成本。
Tasks with few dependencies are often described as "embarrassingly parallel," meaning that they're almost too easy to separate into a number of parallel tasks.
依赖性较少的任务通常被描述为“高度平行”，即太容易将它们分成多个并行任务了。
Sometimes tasks may need to be conducted in parallel and must be on their own tab; still others can be sufficiently focused that there is no benefit from additional components.
有时任务需要并行进行，且必须在各自的选项卡上；而不能从附加组件获益的其他任务则得不到关注。
Creating the process flow by connecting the tasks together (as sequential or parallel steps).
通过将任务连接起来(作为连续或相同的步骤)创建流程流。
These three tasks can be done at the same time, so we add a Parallel Gateway to our Process.
这三项任务可同时完成，因此我们为流程添加一个ParallelGateway。
Because GPars gives these tasks to a pool of workers, I can now perform all of my searches in parallel (provided the pool is large enough).
由于GPars将这些任务分配给了一批作业员，我现在可以并行执行我的所有搜索了(只要内存池足够大)。
It started way back in JDK 1.5 when Doug Lea and his JSR 166 expert group added their API level support for breaking up an application into tasks that could be executed in parallel.
这起始于jdk 1.5，那时DougLea和他的JSR 166专家组添加了API层次支持以将应用分解为多个可以并行执行的任务。
The application part which is designed to run in parallel to other tasks should go into a Work object, which is an object implementing the com.ibm.websphere.asynchbeans.Work interface.
与其他任务并行运行的应用程序部分应该加入Work对象，它是实现com . ibm . websphere . asynchbeans .Work接口的对象。
WebSphere Application Server is designed to process a large number of parallel short-time tasks, and work queues and thread pools are two components that are used often to handle parallel workload.
WebSphereApplicationServer旨在处理大量的并行短时间任务，工作队列和线程池是两个经常用来处理并行工作负载的组件。
Workflows enable the administrator to create tasks that can run either in sequence or in parallel across multiple computers at the same time.
工作流让管理员可以创建任务，这些任务或者可以依次运行，也可以同时在多台计算机上并行运行。
This provides a mechanism for developers to decompose problems into tasks that can then be executed in parallel across arbitrary Numbers of processor cores.
它为开发者提供了一种机制，可以将问题拆解为多个任务，在任意数量的处理器核心上并行执行。
For example, if two tasks do not depend on each other, they could be done by two different resources in parallel, making the overall process run faster.
例如，如果两个任务没有相互依赖关系，则它们可以由两个不同的资源并行完成，从而使总体流程运行得更快。
With this method a single query causes DB2 to generate several tasks, which are executed in parallel to access the data.
通过这种方法，一条查询可以使DB2生成多个任务，这些任务被并行地执行，以访问数据。
Because most of the tasks are automated, there are little or no constraints on the number of them that can be performed in parallel.
由于大多数任务都是自动执行的，对可以并行执行的资源数量几乎没有约束。
Utilizing a pool of workers to execute multiple tasks in parallel is also beneficial for CPU-bound tasks.
使用一批作业员来并行执行多个任务对cpu密集型任务也大有裨益。
They are well suited to sequential and parallel tasks.
它们非常适合于顺序执行或者并发执行的任务。
In addition, if you use a compute grid to process tasks in parallel, you usually want a data grid superimposed as well, to provision the state for the compute grid to work off.
此外，如果使用计算网格并行处理任务，通常也需要一个数据网格来为其提供清理状态。
Then we use "Async.Run" to run the tasks in parallel.
接着，我们使用“Async . Run”来并行运行这些任务。
A program that, for instance, wants to use something like Skynet, a Ruby version of MapReduce, using multiple VMs to run tasks in parallel, will have to be adapted for every Ruby implementation.
举例来说，如果一个程序想要使用Skynet (MapReduce的Ruby版本)，还想使用多虚拟机来并行运行任务，那么其必须进行适配以适合每种Ruby实现。
应用推荐