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Issue No.10 - Oct. (2012 vol.45)
pp: 72-80
Moustafa AbdelBaky , Rutgers University
Manish Parashar , Rutgers University
Hyunjoo Kim , Xerox Research Center Webster
Kirk E. Jordan , IBM T.J. Watson Research Center
Vipin Sachdeva , IBM T.J. Watson Research Center
James Sexton , IBM T.J. Watson Research Center
Hani Jamjoom , IBM T.J. Watson Research Center
Zon-Yin Shae , IBM T.J. Watson Research Center
Gergina Pencheva , University of Texas at Austin
Reza Tavakoli , University of Texas at Austin
Mary F. Wheeler , University of Texas at Austin
With the right software infrastructure, clouds can provide scientists with "as a service" access to high-performance computing resources. An award-winning prototype framework transforms the Blue Gene/P system into an elastic cloud to run a representative HPC application. The Web extra at is a video demonstrating an award-winning prototype framework that transforms the Blue Gene/P system into an elastic cloud to run a representative high-performance computing application.
Software architecture, Cloud computing, High performance computing, Service oriented architecture, ensemble applications, cloud computing, high-performance computing, HPC as a service
Moustafa AbdelBaky, Manish Parashar, Hyunjoo Kim, Kirk E. Jordan, Vipin Sachdeva, James Sexton, Hani Jamjoom, Zon-Yin Shae, Gergina Pencheva, Reza Tavakoli, Mary F. Wheeler, "Enabling High-Performance Computing as a Service", Computer, vol.45, no. 10, pp. 72-80, Oct. 2012, doi:10.1109/MC.2012.293
1. M. Parashar et al., Cloud Paradigms and Practices for CDS&E, research report, Cloud and Autonomic Computing Center, Rutgers Univ., 2012.
2. K. Yelick et al., The Magellan Report on Cloud Computing for Science, US Dept. of Energy, 2011; MagellanFinalReport.pdf.
3. Z.-Y. Shae et al., On the Design of a Deep Computing Service Cloud, research report RC24991, IBM T.J. Watson Research Center, 2010; papers/F236CB47BA3A98E5852577270057EEEA/ $Filerc24991.pdf.
4. H. Kim and M. Parashar, “CometCloud: An Autonomic Cloud Engine,” Cloud Computing: Principles and Paradigms, R. Buyya, J. Broberg, and A. Goscinski eds., Wiley, 2011, pp. 275-297.
5. D. Kumar, Z.-Y. Shae, and H. Jamjoom, “Scheduling Batch and Heterogeneous Jobs with Runtime Elasticity in a Parallel Processing Environment,” Proc. 26th Int'l Parallel and Distributed Processing Symp. Workshops and PhD Forum (IPDPSW 12), IEEE, 2012, pp. 65-78.
6. D. Saure et al., “Time-Of-Use Pricing Policies for Offering Cloud Computing as a Service,” Proc. 2010 Int'l Conf. Service Operations and Logistics and Informatics (SOLI 10), IEEE, 2010, pp. 300-305.
7. S.I. Aanonsen et al., “The Ensemble Kalman Filter in Reservoir Engineering—A Review,” SPE J., Sept. 2009, pp. 393-412.
8. M.F. Wheeler, “Advanced Techniques and Algorithms for Reservoir Simulation, II: The Multiblock Approach in the Integrated Parallel Accurate Reservoir Simulator (IPARS),” Resource Recovery, Confinement, and Remediation of Environmental Hazards, J. Chadam et al., eds, Springer, 2002, pp. 9-20.
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