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| 001 | 844901 | ||
| 005 | 20210129233136.0 | ||
| 024 | 7 | _ | |a 10.1016/j.parco.2018.03.002 |2 doi |
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| 037 | _ | _ | |a FZJ-2018-02243 |
| 082 | _ | _ | |a 004 |
| 100 | 1 | _ | |a Narasimhamurthy, Sai |0 P:(DE-HGF)0 |b 0 |e Corresponding author |
| 245 | _ | _ | |a SAGE: Percipient Storage for Exascale Data Centric Computing |
| 260 | _ | _ | |a Amsterdam [u.a.] |c 2019 |b North-Holland, Elsevier Science |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 520 | _ | _ | |a 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. |
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| 700 | 1 | _ | |a Rivas-Gomez, Sergio |0 P:(DE-HGF)0 |b 5 |
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| 773 | _ | _ | |a 10.1016/j.parco.2018.03.002 |g p. S0167819118300772 |0 PERI:(DE-600)1466340-5 |p 22-33 |t Parallel computing |v 83 |y 2019 |x 0167-8191 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/844901/files/1805.00556.pdf |y OpenAccess |
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