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SAGE: Percipient Storage for Exascale Data Centric Computing

机译:SAGE:Exascale数据中心计算的Percipient Storage

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We aim to implement a Big Data/Extreme Computing (BDEC) capable system infrastructure as we head towards the era of Exascale computing - termed SAGE (Percipient StorAGe for Exascale Data Centric Computing). The SAGE system will be capable of storing and processing immense volumes of data at the Exascale regime, and provide the capability for Exascale class applications to use such a storage infrastructure.SAGE addresses the increasing overlaps between Big Data Analysis and HPC in an era of next-generation data centric computing that has developed due to the proliferation of massive data sources, such as large, dispersed scientific instruments and sensors, whose data needs to be processed, analysed and integrated into simulations to derive scientific and innovative insights. Indeed, Exascale I/O, as a problem that has not been sufficiently dealt with for simulation codes, is appropriately addressed by the SAGE platform.The objective of this paper is to discuss the software architecture of the SAGE system and look at early results we have obtained employing some of its key methodologies, as the system continues to evolve. (C) 2018 Elsevier B.V. All rights reserved.
机译:我们的目标是实现一个大数据/极端计算(BDEC)能力的系统基础设施,因为我们朝向Exascale计算定期的Sage的时代(用于Exascale数据中心计算的Percipient Storage)。 SAGE系统将能够在ExaScale制度存储和处理巨大的数据量,并为Exascale类应用程序提供使用这种存储基础结构的能力。搜索在下一个时代的eRA中的大数据分析和HPC之间的增加的重叠。 -Generation数据计算,该计算由于大规模数据源的增殖而产生的,例如大型分散的科学仪器和传感器,其数据需要进行处理,分析和集成模拟,以获得科学和创新的见解。事实上,作为尚未充分处理模拟代码的问题,SAGE平台适当地解决了exaScale I / O.本文的目的是讨论SAGE系统的软件架构,并查看早期结果已经获得了一些关键方法,因为系统继续发展。 (c)2018年elestvier b.v.保留所有权利。

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