在算法上作了较详细的描述。在程序结构上提出了多任务并行处理的程序设计方法。
It provides a detailed description in algorithm and proposed a multiprogramming approach in program structure.
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。
A managed programming model for data parallelism, task parallelism, and coordination on parallel hardware unified by a common work scheduler.
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。
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.
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。
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.
Fork将处理流程拆分为两个或多个可选路径,使两个或多个任务能够并行执行。
Fork splits the process flow into two or more alternative paths, enabling two or more tasks to be performed 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。
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.
其中两个核心组件是用于存储数据的HadoopDistributedFile System (Hadoop分布式文件系统)和用于写入并行处理任务的HadoopMapReduce。
The two core components are the Hadoop Distributed File System for storing data and Hadoop MapReduce for writing parallel-processing jobs.
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。
This provides a mechanism for developers to decompose problems into tasks that can then be executed in parallel across arbitrary Numbers of processor cores.
当完成一个复杂任务需要的处理时间比希望的长的时候,一些系统采用并行处理进程的各部分。
When the processing time required to complete portions of a complex task is longer than desired, some systems handle parts of the processing in parallel.
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。
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.
正如之前提到的,并行性指的是任务的并行处理能力,其中任务会分成不同的子任务,它们最后会合并为一个。
As mentioned before, parallelism implies the parallel-processing capability of a task where the task is split to different subtasks with consolidation at the end.
策略3:并行批处理将批处理任务划分为子任务发送到各个网格节点上,然后再聚合各个部分的结果。
Strategy 3: Parallel Batch subdivides batch work into subjobs to be sent to grid nodes and afterwards aggregates the partial results.
它不使用专门优化过的向量硬件,而是使用标准的标量处理器,但是它采用了大量的处理器来并行处理多个计算任务。
Instead of using specially optimized vector hardware, it USES standard scalar processors, but in large Numbers to perform several computations in parallel.
处于并行状态的多个任务明显表明存在并行处理。
Multiple jobs in parallel status clearly indicate parallel processing.
CP并行可以在db2子系统中为一条查询使用多个任务,而sysplex查询并行这种方法使一个DB 2数据共享组中的所有成员可以一起处理一个查询。
While query CP parallelism used multi-tasking for a query within a DB2 subsystem, this method enables all the members of a DB2 data-sharing group to process a single query.
从理论上来说,您可以采用无数的硬件来处理并行执行的部分,甚至在接近0的时间内完成这些任务,但是对于串行部分来说,这样做不会有任何提高。
In theory you could apply an infinite amount of hardware to do the parallel part in zero time, but the sequential part will see no improvements.
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。
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.
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。
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.
尽管Google需要对搜索以及其它服务进行快速响应,并行处理能够完成这一任务需要,虽然有可能单个线程的速度并不快。
Although Google requires a fast response for search and other services, its parallelism can produce that even if a single sequence of instructions, called a thread, is relatively slow.
让CPU 不参与数据拷贝工作而去处理其他的任务的同时,将数据拷贝任务在CPU 的其他区域与CPU并行工作来极大地提升性能。
Performance is enhanced by allowing the CPU to move on to other tasks while data copying procedds in parallel in another part of the machine .
与传统并行计算研究对象不同的是,空时自适应处理(STAP)的各阶段是由多个相同的、可分离的任务组成。
Different from traditional parallel computations algorithms, Space-Time Adaptive Processing (STAP) algorithms are composed of several phases and each phase consists of many identical, separable tasks.
这样数据交换和任务处理就可以并行进行,从而就缩短了各成员机因负载不均匀而造成的停机等待时间。
In this way, data exchange and task processing can be executed parallelly, so the halt wailing time caused by the unbalanced loads on member computers can be reduced.
任务调度问题是指根据一定的调度策略,把一组并行处理的任务按规定的时序分配到系统的多个处理机节点上,以期获得较好的系统执行性能。
Task scheduling aims at scheduling a set of partially ordered computational tasks onto a multiprocessor system by a given strategy in order to obtain a better system performance.
并行空间连接处理由三个阶段组成:任务创建,任务分配和任务并行执行。
Generally, parallel spatial processing includes three phases: task creation, task assignment and parallel task execution.
主要技术包括:多线程技术并行处理用户交互、晶闸管数字触发、PID参数的自整定等任务;
The main techniques are: multithread technique to parallel handle three task of user interface, thyristor digital trigger and adjust PID parameter automatically;
在此平台的基础上通过对信号处理算法的并行设计,以及对处理任务的合理分配,实现了高速实时雷达信号处理。
Then, basing on this platform, a high speed real-time digital signal processing of radar is carried out, making good use of the parallel design of algorithm and a reasonable assignment of tasks.
介绍了线程集成技术在通用单片微处理器或微控制器上实现实时任务并行性的新方法。
The thread integration technique-a new method for providing real time task concurrency on single microcontrollers and microprocessors is presented.
同时,通过对多处理机任务之间的并行关系的分析,得到了一般最优调度的下界。
At the same time, through the analysis of the parallel relation among the multiprocessor parallel job, the low bound of the optimal schedule has been provided.
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。
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。
While there are already tools available that enable parallel processing, these tools are largely dedicated to task parallel models.
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