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Runtime system design of decoupled execution paradigm for data-intensive high-end computing

机译:数据密集型高端计算的解耦执行范例的运行时系统设计

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High performance computing are widely used for scientific discoveries by running scientific computation programs. Many of these applications are getting more and more data intensive [1]. They generate or access huge amount of data during some execution phases. However, traditional supercomputers are designed for computing-intensive tasks. They usually have highdensity clusters of processing cores and their storage systems are placed remotely and connected to the computing clusters with networks. This separation of the computing system and the storage system causes the data Input/Output performance bottleneck, especially for the data-intensive phases of HPC applications. This bottleneck degrades the HPC system's efficiency.
机译:高性能计算通过运行科学计算程序而广泛用于科学发现。这些应用程序中的许多正在变得越来越密集的数据[1]。它们在某些执行阶段会生成或访问大量数据。但是,传统的超级计算机是为处理计算密集型任务而设计的。它们通常具有高密度的处理核心集群,并且它们的存储系统被远程放置并通过网络连接到计算集群。计算系统和存储系统的这种分离导致数据输入/输出性能瓶颈,特别是对于HPC应用程序的数据密集型阶段。这个瓶颈降低了HPC系统的效率。

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