A Pipelining Implementation for High Resolution Seismic Hazard Maps Production
Author(s) -
Yelena Kropivnitskaya,
Jinhui Qin,
K. F. Tiampo,
Michael Bauer
Publication year - 2015
Publication title -
procedia computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.334
H-Index - 76
ISSN - 1877-0509
DOI - 10.1016/j.procs.2015.05.337
Subject(s) - computer science , stream processing , workload , scalability , hazard , real time computing , supercomputer , distributed computing , parallel computing , database , operating system , chemistry , organic chemistry
eismic hazard maps are a significant input into emergency hazard management that play an important role in saving human lives and reducing the economic effects after earthquakes. Despite the fact that a number of software tools have been developed (McGuire, 1976, 1978; Bender & Perkins, 1982, 1987; Robinson et al., 2005, 2006; Field et al., 2003), map resolution is generally low, potentially leading to uncertainty in calculations of ground motion level and underestimation of the seismic hazard in a region. In order to generate higher resolution maps, the biggest challenge is to handle the significantly increased data processing workload.In this study, a method for improving seismic hazard map resolution is presented that employs a pipelining implementation of the existing EqHaz program suite (Assatourians & Atkinson, 2013) based on IBM InfoSphere Streams–an advanced stream computing platform. Its architecture is specifically configured for continuous analysis of massive volumes of data at high speeds and low latency. Specifically, it treats processing workload as data streams. Processing procedures are implemented as operators that are connected to form processing pipelines. To handle large processing workload, these pipelines are flexible and scalable to be deployed and run in parallel on large-scale HPC clusters to meet application performance requirements. As a result, mean hazard calculations are possible for maps with resolution up to 2,500,000 points with near-real-time processing time of approximately 5-6minutes
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