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Title: Finding Tropical Cyclones on a Cloud Computing Cluster: Using Parallel Virtualization for Large-Scale Climate Simulation Analysis

Abstract

Extensive computing power has been used to tackle issues such as climate changes, fusion energy, and other pressing scientific challenges. These computations produce a tremendous amount of data; however, many of the data analysis programs currently only run a single processor. In this work, we explore the possibility of using the emerging cloud computing platform to parallelize such sequential data analysis tasks. As a proof of concept, we wrap a program for analyzing trends of tropical cyclones in a set of virtual machines (VMs). This approach allows the user to keep their familiar data analysis environment in the VMs, while we provide the coordination and data transfer services to ensure the necessary input and output are directed to the desired locations. This work extensively exercises the networking capability of the cloud computing systems and has revealed a number of weaknesses in the current cloud system software. In our tests, we are able to scale the parallel data analysis job to a modest number of VMs and achieve a speedup that is comparable to running the same analysis task using MPI. However, compared to MPI based parallelization, the cloud-based approach has a number of advantages. The cloud-based approach is more flexiblemore » because the VMs can capture arbitrary software dependencies without requiring the user to rewrite their programs. The cloud-based approach is also more resilient to failure; as long as a single VM is running, it can make progress while as soon as one MPI node fails the whole analysis job fails. In short, this initial work demonstrates that a cloud computing system is a viable platform for distributed scientific data analyses traditionally conducted on dedicated supercomputing systems.« less

Authors:
; ; ;
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
Computational Research Division
OSTI Identifier:
1004602
Report Number(s):
LBNL-4218E
TRN: US1100622
DOE Contract Number:  
DE-AC02-05CH11231
Resource Type:
Conference
Resource Relation:
Conference: 2nd IEEE International Conference on Cloud Computing Technology and Science (CloudCom 2010) , Indianapolis, Indiana, USA, Nov 30 - Dec 3, 2010
Country of Publication:
United States
Language:
English
Subject:
97; CLIMATES; CLOUDS; DATA ANALYSIS; PRESSING; SIMULATION; THERMONUCLEAR REACTORS; Tropical cyclones, cloud computing, distributed data processing

Citation Formats

Hasenkamp, Daren, Sim, Alexander, Wehner, Michael, and Wu, Kesheng. Finding Tropical Cyclones on a Cloud Computing Cluster: Using Parallel Virtualization for Large-Scale Climate Simulation Analysis. United States: N. p., 2010. Web.
Hasenkamp, Daren, Sim, Alexander, Wehner, Michael, & Wu, Kesheng. Finding Tropical Cyclones on a Cloud Computing Cluster: Using Parallel Virtualization for Large-Scale Climate Simulation Analysis. United States.
Hasenkamp, Daren, Sim, Alexander, Wehner, Michael, and Wu, Kesheng. Thu . "Finding Tropical Cyclones on a Cloud Computing Cluster: Using Parallel Virtualization for Large-Scale Climate Simulation Analysis". United States. doi:. https://www.osti.gov/servlets/purl/1004602.
@article{osti_1004602,
title = {Finding Tropical Cyclones on a Cloud Computing Cluster: Using Parallel Virtualization for Large-Scale Climate Simulation Analysis},
author = {Hasenkamp, Daren and Sim, Alexander and Wehner, Michael and Wu, Kesheng},
abstractNote = {Extensive computing power has been used to tackle issues such as climate changes, fusion energy, and other pressing scientific challenges. These computations produce a tremendous amount of data; however, many of the data analysis programs currently only run a single processor. In this work, we explore the possibility of using the emerging cloud computing platform to parallelize such sequential data analysis tasks. As a proof of concept, we wrap a program for analyzing trends of tropical cyclones in a set of virtual machines (VMs). This approach allows the user to keep their familiar data analysis environment in the VMs, while we provide the coordination and data transfer services to ensure the necessary input and output are directed to the desired locations. This work extensively exercises the networking capability of the cloud computing systems and has revealed a number of weaknesses in the current cloud system software. In our tests, we are able to scale the parallel data analysis job to a modest number of VMs and achieve a speedup that is comparable to running the same analysis task using MPI. However, compared to MPI based parallelization, the cloud-based approach has a number of advantages. The cloud-based approach is more flexible because the VMs can capture arbitrary software dependencies without requiring the user to rewrite their programs. The cloud-based approach is also more resilient to failure; as long as a single VM is running, it can make progress while as soon as one MPI node fails the whole analysis job fails. In short, this initial work demonstrates that a cloud computing system is a viable platform for distributed scientific data analyses traditionally conducted on dedicated supercomputing systems.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Sep 30 00:00:00 EDT 2010},
month = {Thu Sep 30 00:00:00 EDT 2010}
}

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