Taming i/o variation on qos-less hpc storage: What can applications do?

Document Type

Conference Proceeding

Publication Date

11-1-2020

Abstract

As high-performance computing (HPC) is being scaled up to exascale to accommodate new modeling and simulation needs, I/O has continued to be a major bottleneck in the end-to-end scientific processes. Nevertheless, prior work in this area mostly aimed to maximize the average performance, and there has been a lack of study and solutions that can manage I/O performance variation on HPC systems. This work aims to take advantage of the storage characteristics and explore application level solutions that are interference-aware. In particular, we monitor the performance of data analytics and estimate the state of shared storage resources using discrete fourier transform (DFT). If heavy I/O interference is predicted to occur at a given timestep, data analytics can dynamically adapt to the environment by lowering the accuracy and performing partial or no augmentation from the shared storage, dictated by an augmentation-bandwidth plot. We evaluate three data analytics, XGC, GenASiS, and Jet, on Chameleon, and quantitatively demonstrate that both the average and variation of I/O performance can be vastly improved using our dynamic augmentation, with the mean and variance improved by as much as 67% and 96%, respectively, while maintaining acceptable outcome of data analysis.

Identifier

85102388894 (Scopus)

ISBN

[9781728199986]

Publication Title

International Conference for High Performance Computing Networking Storage and Analysis Sc

External Full Text Location

https://doi.org/10.1109/SC41405.2020.00015

e-ISSN

21674337

ISSN

21674329

Volume

2020-November

Grant

1718297

Fund Ref

National Science Foundation

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