Storage wall for exascale supercomputing

The mismatch between compute performance and I/O performance has long been a stumbling block as supercomputers evolve from petaflops to exaflops. Currently, many parallel applications are I/O intensive, and their overall running times are typically limited by I/O performance. To quantify the I/O per...

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Bibliographic Details
Published in:Frontiers of information technology & electronic engineering 2016-11, Vol.17 (11), p.1154-1175
Main Authors: Hu, Wei, Liu, Guang-ming, Li, Qiong, Jiang, Yan-huang, Cai, Gui-lin
Format: Article
Language:English
Subjects:
Classification
Communications Engineering
Computer Hardware
Computer Science
Computer Systems Organization and Communication Networks
Design optimization
Electrical Engineering
Electronics and Microelectronics
Evolution
Existence theorems
Extrapolation
Hardware
Information technology
Instrumentation
Networks
Optimization
Supercomputers
Systems design
Walls
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Summary:The mismatch between compute performance and I/O performance has long been a stumbling block as supercomputers evolve from petaflops to exaflops. Currently, many parallel applications are I/O intensive, and their overall running times are typically limited by I/O performance. To quantify the I/O performance bottleneck and highlight the significance of achieving scalable performance in peta/exascale supercomputing, in this paper, we introduce for the first time a formal definition of the ‘storage wall’ from the perspective of parallel application scalability. We quantify the effects of the storage bottleneck by providing a storage-bounded speedup, defining the storage wall quantitatively, presenting existence theorems for the storage wall, and classifying the system architectures depending on I/O performance variation. We analyze and extrapolate the existence of the storage wall by experiments on Tianhe-1A and case studies on Jaguar. These results provide insights on how to alleviate the storage wall bottleneck in system design and achieve hardware/software optimizations in peta/exascale supercomputing.
ISSN:2095-9184
2095-9230
DOI:10.1631/FITEE.1601336