流体系结构是一种新型体系结构，它对科学计算和媒体处理等具有大规模数据并行性的应用具有很高的计算性能。
Stream architecture is a emerging computer architecture which has enormous performance for applications with great data parallelism such as scientific computation and media processing.
策略是将所有表空间的数据扩展到尽可能多的驱动器中，以最大化IO并行性。
The strategy is to spread the data for all tablespaces across as many drives as possible to maximize the IO parallelism.
若启用分区间并行性，对于任何给定的查询，DB2首先识别数据所驻留的分区。
With inter-partition parallelism, for any given query, DB2 first identifies the partitions where the data resides.
这种并行性（或多线程）问题在于确保您希望为其更新信息的线程，仅影响正在处理的数据和信息。
This issue with concurrency (or multi-threading) is ensuring that threads that you want to update the information with only affect the data and information you are processing.
如Google框架MapReduce；MR描述了一种使用Map功能实现并行性的方法，它将大型数据分割成多个键-值对。
Like the Google framework MapReduce; MR describes a way of implementing parallelism using the Map function which splits a large data into multiple key-value pairs.
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。
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.
要启用优化器以选择分区内并行性，您必须按如下所示设置分区内并行的数据库管理器配置参数。
To enable the optimizer to choose the intra-partition parallelism you must set the intra_parallel database manager configuration parameter as shown here.
数据库配置参数dft_degree指定每个数据库默认的并行性级别。
The dft_degree database configuration parameter specifies the default level of parallelism for each database.
这意味着Clustrix群集数据库应该能够以最大的并行性执行查询语句，许多同步查询具有最大的并发性。
That means the Clustrix Clustered Database is supposed to execute the query with maximum parallelism and many simultaneous queries with maximum concurrency.
本文的设计着重从多个层次利用并行处理技术来提高环路滤波的速度，包括流水线设计、数据流驱动控制策略以及算法并行性设计。
Our design emphasizes on using parallel processing technology from multi-level to improve speed, including pipelining design, data-flow drive strategy and algorithmic parallelism design.
现代高性能微处理器已经能够开发出越来越多的并行性，当一段程序因等待访存数据而停顿时，处理器可以选择执行其他程序段。
Modern high-performance processor can exploit more parallelism. As one program stops due to waiting for memory data, processor can choose to run another program.
为了进一步地发掘嵌套循环的并行性和数据访问的时间局部性，对算法增加了一个线性循环变换的功能模块来进行改进。
To further excavate the nesting loop parallelism and the data access-time locality, a linearity cyclic transformation functional module is used to improve the algorithm.
为分析循环程序的并行性，最重要且最基本的工作是数据相关性分析，而数据相关性分析最主要的内容之一是循环程序中数组元素间的数据相关性分析。
In analysing the parallelism of loop program the analysis of data dependency, which mainly concerns the analysis of a set of elements in loop program is the most important and fundamental.
索引复制是分布并行数据库提供并行性和提高可用性的一个重要手段。
Index replication is an important approach that provides parallel and improves usability of distributed parallel database.
首先介绍了L SSIMD阵列微处理器的三种并行性：数据并行、流水线并行和指令的并行执行。
This paper firstly discusses three types of parallelism in LS SIMD array microprocessor, they are the concurrence of data, the pipelining and the operation in parallel.
最后，通过扩展基于控制表的数据库同步方案，将研究成果应用其上以提高可并行性，证明了模型的可行性。
Finally, by extending the synchronization system based on control table and applying the dependence model, the performance in parallel is improved greatly.
在此模型的基础上又提出了繁衍层次的概念，从而为数据库操作片段的可并行性分析提供了基础，并设计了一个数据库操作片段相关性识别算法。
Then, generation of database operations is proposed to provide the theoretical principle of parallelism. Also, algorithm to identify the dependence of database operations is designed.
该算法复杂度低，并行性好，支持高速低功耗硬件实现，能实现对空间数据的实时处理，在空间探测领域具有良好的应用前景。
The algorithm has lower complexities and good parallel architecture, and is suitable for the high-speed low-power hardware realization and real-time processing.
该算法复杂度低，并行性好，支持高速低功耗硬件实现，能实现对空间数据的实时处理，在空间探测领域具有良好的应用前景。
The algorithm has lower complexities and good parallel architecture, and is suitable for the high-speed low-power hardware realization and real-time processing.
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