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SAGE: Percipient Storage for Exascale Data Centric Computing

机译:SAGE:百亿亿次数据中心计算的永久存储

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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:在迈向百亿亿次计算时代(称为SAGE(百亿亿次数据中心计算的Perstoient StorAGe))的时代,我们旨在实现具有大数据/极限计算(BDEC)功能的系统基础架构。 SAGE系统将能够在Exascale体制下存储和处理海量数据,并为Exascale类应用程序提供使用这种存储基础架构的能力。SAGE解决了在下一个时代大数据分析与HPC之间日益重叠的问题。新一代以数据为中心的计算是由于海量数据源(例如大型,分散的科学仪器和传感器)的激增而发展起来的,需要对它们的数据进行处理,分析和集成到仿真中以得出科学和创新的见解。的确,SAGE平台可以适当地解决Exascale I / O这个尚未被仿真代码充分处理的问题。本文的目的是讨论SAGE系统的软件体系结构并查看我们的早期结果。随着系统的不断发展,已经采用了一些关键方法。 (C)2018 Elsevier B.V.保留所有权利。

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