To scale search and indexing operations, you can deploy multiple instances of the index data service to different servers.
要缩放搜索和索引操作，您可以将多个索引数据服务实例部署到不同的服务器。
While the RDBMS provides a rock-solid foundation for storing data in traditional client-server architectures, it doesn't easily (or cheaply) scale to multiple nodes.
尽管RDBMS为在传统的客户端-服务器架构中存储数据提供了一个坚实的基础，但它不能轻松地(或便宜地)扩展到多个节点。
WebSphere eXtreme Scale operates as an in-memory data grid that dynamically caches, partitions, replicates, and manages application data and business logic across multiple servers.
可以将WebSphereeXtremeScale作为一个内存中的数据网格来操作。它能跨多个服务器动态缓存、分区、复制和管理应用数据与业务逻辑。
Thus, for caching a large amount of highly available partitioned data, WebSphere eXtreme Scale needs to be deployed in multiple JVMs.
因此，要缓存大量的高可用性分区数据，需要将WebSphereeXtremeScale部署到多个JVM中。
Its advanced scale out capability coupled with automated data placement and unified management enables customers to rapidly expand storage infrastructure to multiple petabytes with minimal efforts.
它把先进的水平扩展能力与自动的数据布置和统一的管理结合在一起，让客户能够方便、快捷地把存储基础设施扩展到数P b级。
And they can be tied together to build large scale applications that require multiple servers to meet the demands of users for data.
它们还能连在一起使用，以构造需要多个服务器满足用户对数据需求的大规模应用系统。
Statistical validation shows that there is great consistency between extracted multiple cropping index and statistical data at different scale. So the results of this dissertation are believable.
统计验证表明，复种指数提取结果与不同尺度的统计数据均有很高的一致性。
The execute model of single instruction, multiple threads (SIMT) of CUDA is very suitable for parallel to execute the same operations for large-scale data;
CUDA的单指令、多线程（SIMT）的执行模型，很适合大型数据上并行执行相同的操作；
The execute model of single instruction, multiple threads (SIMT) of CUDA is very suitable for parallel to execute the same operations for large-scale data;
CUDA的单指令、多线程（SIMT）的执行模型，很适合大型数据上并行执行相同的操作；
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