Exascale Storage Systems the SIRIUS Way

27th IUPAP Conference on Computational Physics

物理学;计算机科学

Klasky, S.A.^1,2,3 ; Abbasi, H.^1 ; Ainsworth, M.^1,4 ; Choi, J.^1 ; Curry, M.^5 ; Kurc, T.^1,6 ; Liu, Q.^1 ; Lofstead, J.^5 ; Maltzahn, C.^7 ; Parashar, M.^8 ; Podhorszki, N.^1 ; Suchyta, E.^1 ; Wang, F.^1 ; Wolf, M.^1,3 ; Chang, C.S.^9 ; Churchill, M.^9 ; Ethier, S.^9

Oak Ridge National Laboratory, Oak Ridge

TN

37831, United States^1

Department of Electrical Engineering and Computer Science, University of Tennessee Knoxville, Knoxville

TN

37996, United States^2

School of Computer Science, Georgia Institute of Technology, Atlanta

GA

30332, United States^3

Division of Applied Mathematics, Brown University, Providence

RI

02912, United States^4

Sandia National Laboratories, Albuquerque

NM

87185, United States^5

Department of Biomedical Informatics, Stony Brook University, Stony Brook

NY

11794, United States^6

Computer Science Department, University of California at Santa Cruz, Santa Cruz

CA

95064, United States^7

Rutgers Discovery Informatics Institute, Rutgers University, Piscataway

NJ

08854, United States^8

Princeton Plasma Physics Laboratory, Princeton

NJ

08543, United States^9

关键词: Critical factors;    Data movements;    Exascale computing;    Large data volumes;    New technologies;    Performance issues;    Storage systems;    Support requirements;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/759/1/012095/pdf
DOI  :  10.1088/1742-6596/759/1/012095

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

As the exascale computing age emerges, data related issues are becoming critical factors that determine how and where we do computing. Popular approaches used by traditional I/O solution and storage libraries become increasingly bottlenecked due to their assumptions about data movement, re-organization, and storage. While, new technologies, such as "burst buffers", can help address some of the short-term performance issues, it is essential that we reexamine the underlying storage and I/O infrastructure to effectively support requirements and challenges at exascale and beyond. In this paper we present a new approach to the exascale Storage System and I/O (SSIO), which is based on allowing users to inject application knowledge into the system and leverage this knowledge to better manage, store, and access large data volumes so as to minimize the time to scientific insights. Central to our approach is the distinction between the data, metadata, and the knowledge contained therein, transferred from the user to the system by describing "utility" of data as it ages.