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Title: An Adaptive Middleware Framework for Scientific Computing at Extreme Scales

Abstract

Large computing systems including clusters, clouds, and grids, provide high-performance capabilities that can be utilized for many applications. But as the ubiquity of these systems increases and the scope of analysis being done on them grows, there is a growing need for applications that 1) do not require users to learn the details of high performance systems, and 2) are flexible and adaptive in their usage of these systems to accommodate the best time-to-solution for end users. We introduce a new adaptive interface design and a prototype implementation within the framework of an established middleware framework, MeDICi, for high performance computing systems and describe the applicability of this adaptive design to a real-life scientific workflow. This adaptive framework provides an access model for implementing a processing pipeline using high performance systems that are not local to the data source, making it possible for the compute capabilities at one site to be applied to analysis on data being generated at another site in an automated process. This adaptive design improves overall time-to-solution by moving the data analysis task to the most appropriate resource dynamically, reacting to failures and load fluctuations.

Authors:
; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States). Environmental Molecular Sciences Lab. (EMSL)
Sponsoring Org.:
USDOE
OSTI Identifier:
988663
Report Number(s):
PNNL-SA-71671
29290; TRN: US201019%%44
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Conference
Resource Relation:
Conference: The 2010 IEEE International Conference on Information Reuse and Integration (IRI 2010) , 232-238
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICAL METHODS AND COMPUTING; ADAPTIVE SYSTEMS; DATA ANALYSIS; DESIGN; COMPUTER NETWORKS; COMPUTER CALCULATIONS; IMPLEMENTATION; PERFORMANCE; Middleware, data intensive computing, service oriented architectures, workflow, scientific workflow, adaptive; Environmental Molecular Sciences Laboratory

Citation Formats

Gosney, Arzu, Oehmen, Christopher S, Wynne, Adam S, and Almquist, Justin P. An Adaptive Middleware Framework for Scientific Computing at Extreme Scales. United States: N. p., 2010. Web. doi:10.1109/IRI.2010.5558934.
Gosney, Arzu, Oehmen, Christopher S, Wynne, Adam S, & Almquist, Justin P. An Adaptive Middleware Framework for Scientific Computing at Extreme Scales. United States. https://doi.org/10.1109/IRI.2010.5558934
Gosney, Arzu, Oehmen, Christopher S, Wynne, Adam S, and Almquist, Justin P. 2010. "An Adaptive Middleware Framework for Scientific Computing at Extreme Scales". United States. https://doi.org/10.1109/IRI.2010.5558934.
@article{osti_988663,
title = {An Adaptive Middleware Framework for Scientific Computing at Extreme Scales},
author = {Gosney, Arzu and Oehmen, Christopher S and Wynne, Adam S and Almquist, Justin P},
abstractNote = {Large computing systems including clusters, clouds, and grids, provide high-performance capabilities that can be utilized for many applications. But as the ubiquity of these systems increases and the scope of analysis being done on them grows, there is a growing need for applications that 1) do not require users to learn the details of high performance systems, and 2) are flexible and adaptive in their usage of these systems to accommodate the best time-to-solution for end users. We introduce a new adaptive interface design and a prototype implementation within the framework of an established middleware framework, MeDICi, for high performance computing systems and describe the applicability of this adaptive design to a real-life scientific workflow. This adaptive framework provides an access model for implementing a processing pipeline using high performance systems that are not local to the data source, making it possible for the compute capabilities at one site to be applied to analysis on data being generated at another site in an automated process. This adaptive design improves overall time-to-solution by moving the data analysis task to the most appropriate resource dynamically, reacting to failures and load fluctuations.},
doi = {10.1109/IRI.2010.5558934},
url = {https://www.osti.gov/biblio/988663}, journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed Aug 04 00:00:00 EDT 2010},
month = {Wed Aug 04 00:00:00 EDT 2010}
}

Conference:
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