创建并行作业并描述其内容。
创建一个新的并行作业。
确认您并行作业的框架与图1类似。
Verify that the skeleton of your parallel appears similar to Figure 1.
将这两个连接器放到并行作业画布的另一面。
Place these two connectors at opposite sides of the parallel job canvas.
将许多并行作业的执行状态从GEE集合报告给PJM。
Execution status of the many parallel jobs is reported to the PJM from the collection of GEEs.
找到XMLInput阶段并将其拖放到并行作业上。
步骤2：创建DataStage并行作业并概括其内容。
Step 2: Create a DataStage parallel job and outline its contents.
向该并行作业添加两个DB 2Connector阶段。
找到Transformer阶段并将该图标拖放到并行作业面板。
Locate the Transformer stage and drag this icon to the parallel job pane.
将DB 2Connector阶段添加到并行作业的右上部分。
Add a DB2 Connector stage to the top right portion of the parallel job.
将DB 2Connector阶段拖放到该并行作业上。
实验结果表明，这种负载平衡方法能够有效地提高并行作业的运行性能。
Experiments show that the method using these algorithms can effectively improve executive performance of the parallel job.
将SequentialFile阶段添加到新并行作业作业设计区的左上部分。
Add a Sequential File stage to the top left portion of the job design area for the new parallel job.
BACKFILL方法在并行作业调度时并不能有效地的利用CPU资源。
The BACKFILL algorithm is not efficient for the CPU utilization of job scheduling in parallel system.
现在，您的DataStage作业的第一部分已经具有一个并行作业骨架。
You now have a parallel job skeleton for the first part of your DataStage job.
为评估计算网格上并行作业的无中心式调度，设计了基于离散事件的模拟系统J3S。
To evaluate decentralized scheduling of parallel jobs on computational grids, the discrete event-based simulation system J3S is designed.
检查您的并行作业设计是否类似于图5，它显示了链接在一起的各个阶段，如步骤6所述
Verify that your parallel job design is similar to Figure 5, which shows the various stages linked together, as described in Step 6
组合使用并行作业管理器(PJM)、n层缓存结构和关联路由，可以提供功能强大的高性能计算网格。
Combining the Parallel Job Manager (PJM), N-tier caching structures, and affinity routing provides a powerful, high-performance compute grid.
该应用程序的多个实例可以使用并行作业管理器(PJM)来进行分派，其中各个分区处理不同的数据部分。
Multiple instances of that application can be dispatched using the Parallel Job Manager (PJM), where each partition processes a different section of the data.
分区还允许并行作业的分配(例如，通过将设计的特定部分分配给一个团队，而将设计的另一部分分配给另一个团队)。
Partitions also allow for the assignment of parallel work (for example, by assigning the design of a particular set of parts to one team, and of another set of parts to another team).
计算网格包括一个称为并行作业管理器(PJM)的特性，您可以使用该特性来定义将较大作业分解为许多较小作业的规则。
Compute Grid includes a feature called Parallel job Manager (PJM) which you can use to define rules for decomposing large jobs into many small jobs.
在这四个用例场景中，每个作业都被设计为并行作业，以便充分利用WebSphereDataStage的并行处理能力。
Each job within the four use-case scenarios is a parallel job designed to take full advantage of WebSphere DataStage's parallel processing capabilities.
并行作业管理器层同样也是计算网格的组成部分，可以将较大的作业分解为较小的分区、并为跨集群执行的分区作业提供操作控制。
Parallel Job Manager tier, also part of Compute Grid, decomposes large jobs into smaller partitions and provides operational control over partitioned jobs executing across the cluster.
相反，管理员应该具有更好的操作控制，在停止逻辑作业时，基础结构将会停止正在并行运行的众多作业。
Instead, the administrator should have better operational control and stop the logical job, where the infrastructure would stop the many jobs running in parallel.
网格计算(包括跨端点网格并行执行作业)的原则随着下一代企业应用程序而出现。
The principles of grid computing, including the parallel execution of a job across a grid of endpoints, emerge as the next generation of enterprise applications.
这种并行集群系统之所以被称为伪远程线程，是因为线程是在作业调度器上调度的，但线程内的代码却是在远程计算机上执行的。
This parallel clustering system is called Pseudo remote threads because the threads are scheduled on the job dispatcher but the code within the threads is executed on a remote machine.
在高并行批处理领域中，比较困难的问题是操作控制，以及管理许多并发执行的作业。
A difficult challenge in the area of highly-parallel batch processing is operational control and managing the many jobs that are executing concurrently.
总体上，集群具备构建、提交、执行和管理串行与并行批作业的能力。
As a whole, the cluster provides the capability to build, submit, execute, and manage serial and parallel batch jobs.
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。
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。
Utilizing a pool of workers to execute multiple tasks in parallel is also beneficial for CPU-bound tasks.
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