NDLI: A Framework for Elastic Execution of Existing MPI Programs

Content Provider	IEEE Xplore Digital Library
Author	Raveendran, A. Bicer, T. Agrawal, G.
Copyright Year	2011
Abstract	There is a clear trend towards using cloud resources in the scientific or the HPC community, with a key attraction of cloud being the {em elasticity} it offers. In executing HPC applications on a cloud environment, it will clearly be desirable to exploit elasticity of cloud environments, and increase or decrease the number of instances an application is executed on during the execution of the application, to meet time and/or cost constraints. Unfortunately, HPC applications have almost always been designed to use a fixed number of resources. This paper describes our initial work towards the goal of making existing MPI applications elastic for a cloud framework. Considering the limitations of the MPI implementations currently available, we support adaptation by terminating one execution and restarting a new program on a different number of instances. The components of our envisioned system include a decision layer which considers time and cost constraints, a framework for modifying MPI programs, and a cloud-based runtime support that can enable redistributing of saved data, and support automated resource allocation and application restart on a different number of nodes. Using two MPI applications, we demonstrate the feasibility of our approach, and show that outputting, redistributing, and reading back data can be a reasonable approach for making existing MPI applications elastic.
Starting Page	940
Ending Page	947
File Size	659114
Page Count	8
File Format	PDF
ISBN	9781612844251
ISSN	15302075
DOI	10.1109/IPDPS.2011.240
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Cloud computing Runtime Elasticity Resource management Arrays Monitoring
Content Type	Text
Resource Type	Article

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