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Title: MOLAR: Adaptive Runtime Support for High-End Computing Operating and Runtime Systems

Abstract

MOLAR is a multi-institutional research effort that concentrates on adaptive, reliable, and efficient operating and runtime system (OS/R) solutions for ultra-scale high-end scientific computing on the next generation of supercomputers. This research addresses the challenges outlined in FAST-OS (forum to address scalable technology for runtime and operating systems) and HECRTF (high-end computing revitalization task force) activities by exploring the use of advanced monitoring and adaptation to improve application performance and predictability of system interruptions, and by advancing computer reliability, availability and serviceability (RAS) management systems to work cooperatively with the OS/R to identify and preemptively resolve system issues. This paper describes recent research of the MOLAR team in advancing RAS for high-end computing OS/Rs.

Authors:
 [1];  [1];  [1];  [2];  [2];  [3];  [3];  [3];  [4];  [4]
  1. ORNL
  2. Louisiana Tech University
  3. North Carolina State University
  4. Ohio State University
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
978167
DOE Contract Number:  
DE-AC05-00OR22725
Resource Type:
Journal Article
Resource Relation:
Journal Name: ACM SIGOPS Operating Systems Review; Journal Volume: 40; Journal Issue: 2
Country of Publication:
United States
Language:
English
Subject:
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; AVAILABILITY; COMPUTERS; MANAGEMENT; MONITORING; PERFORMANCE; RELIABILITY; SUPERCOMPUTERS

Citation Formats

Engelmann, Christian, Scott, Steven L, Bernholdt, David E, Gottumukkala, Narasimha R., Chokchai, Leangsuksun, Varma, Jyothish S., Wang, Chao, Mueller, Frank, Shet, Aniruddha G., and Sadayappan, Ponnuswamy. MOLAR: Adaptive Runtime Support for High-End Computing Operating and Runtime Systems. United States: N. p., 2006. Web. doi:10.1145/1131322.1131337.
Engelmann, Christian, Scott, Steven L, Bernholdt, David E, Gottumukkala, Narasimha R., Chokchai, Leangsuksun, Varma, Jyothish S., Wang, Chao, Mueller, Frank, Shet, Aniruddha G., & Sadayappan, Ponnuswamy. MOLAR: Adaptive Runtime Support for High-End Computing Operating and Runtime Systems. United States. doi:10.1145/1131322.1131337.
Engelmann, Christian, Scott, Steven L, Bernholdt, David E, Gottumukkala, Narasimha R., Chokchai, Leangsuksun, Varma, Jyothish S., Wang, Chao, Mueller, Frank, Shet, Aniruddha G., and Sadayappan, Ponnuswamy. Sun . "MOLAR: Adaptive Runtime Support for High-End Computing Operating and Runtime Systems". United States. doi:10.1145/1131322.1131337.
@article{osti_978167,
title = {MOLAR: Adaptive Runtime Support for High-End Computing Operating and Runtime Systems},
author = {Engelmann, Christian and Scott, Steven L and Bernholdt, David E and Gottumukkala, Narasimha R. and Chokchai, Leangsuksun and Varma, Jyothish S. and Wang, Chao and Mueller, Frank and Shet, Aniruddha G. and Sadayappan, Ponnuswamy},
abstractNote = {MOLAR is a multi-institutional research effort that concentrates on adaptive, reliable, and efficient operating and runtime system (OS/R) solutions for ultra-scale high-end scientific computing on the next generation of supercomputers. This research addresses the challenges outlined in FAST-OS (forum to address scalable technology for runtime and operating systems) and HECRTF (high-end computing revitalization task force) activities by exploring the use of advanced monitoring and adaptation to improve application performance and predictability of system interruptions, and by advancing computer reliability, availability and serviceability (RAS) management systems to work cooperatively with the OS/R to identify and preemptively resolve system issues. This paper describes recent research of the MOLAR team in advancing RAS for high-end computing OS/Rs.},
doi = {10.1145/1131322.1131337},
journal = {ACM SIGOPS Operating Systems Review},
number = 2,
volume = 40,
place = {United States},
year = {Sun Jan 01 00:00:00 EST 2006},
month = {Sun Jan 01 00:00:00 EST 2006}
}