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Resource management for extreme scale high performance computing systems in the presence of failures

dc.contributor.authorDauwe, Daniel, author
dc.contributor.authorPasricha, Sudeep, advisor
dc.contributor.authorSiegel, H. J., advisor
dc.contributor.authorMaciejewski, Anthony A., committee member
dc.contributor.authorBurns, Patrick J., committee member
dc.description.abstractHigh performance computing (HPC) systems, such as data centers and supercomputers, coordinate the execution of large-scale computation of applications over tens or hundreds of thousands of multicore processors. Unfortunately, as the size of HPC systems continues to grow towards exascale complexities, these systems experience an exponential growth in the number of failures occurring in the system. These failures reduce performance and increase energy use, reducing the efficiency and effectiveness of emerging extreme-scale HPC systems. Applications executing in parallel on individual multicore processors also suffer from decreased performance and increased energy use as a result of applications being forced to share resources, in particular, the contention from multiple application threads sharing the last-level cache causes performance degradation. These challenges make it increasingly important to characterize and optimize the performance and behavior of applications that execute in these systems. To address these challenges, in this dissertation we propose a framework for intelligently characterizing and managing extreme-scale HPC system resources. We devise various techniques to mitigate the negative effects of failures and resource contention in HPC systems. In particular, we develop new HPC resource management techniques for intelligently utilizing system resources through the (a) optimal scheduling of applications to HPC nodes and (b) the optimal configuration of fault resilience protocols. These resource management techniques employ information obtained from historical analysis as well as theoretical and machine learning methods for predictions. We use these data to characterize system performance, energy use, and application behavior when operating under the uncertainty of performance degradation from both system failures and resource contention. We investigate how to better characterize and model the negative effects from system failures as well as application co-location on large-scale HPC computing systems. Our analysis of application and system behavior also investigates: the interrelated effects of network usage of applications and fault resilience protocols; checkpoint interval selection and its sensitivity to system parameters for various checkpoint-based fault resilience protocols; and performance comparisons of various promising strategies for fault resilience in exascale-sized systems.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.publisherColorado State University. Libraries
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see
dc.subjecthigh performance computing
dc.subjectHPC resilience
dc.subjectapplication performance modeling
dc.subjectresource management
dc.subjectHPC networking
dc.titleResource management for extreme scale high performance computing systems in the presence of failures
dcterms.rights.dplaThis Item is protected by copyright and/or related rights ( You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). and Computer Engineering State University of Philosophy (Ph.D.)


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