The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.06 - Nov.-Dec. (2013 vol.15)
pp: 46-54
Scott Simmerman , Univ. of Tennessee, Knoxville, TN, USA
James Osborne , Univ. of Tennessee, Knoxville, TN, USA
Jian Huang , Univ. of Tennessee, Knoxville, TN, USA
ABSTRACT
As multiprocessor and multicore technology becomes prevalent, shared-memory architectures with 1,024 or more processing cores are becoming available for general-purpose applications. As an early operator of such a system, the Remote Data Analysis and Visualization (RDAV) center at the University of Tennessee observed a host of user applications needing to scale up their computation by running many concurrent instances of generic codes. This isn't a typical way of using high-performance computing systems, and naive solutions supporting such needs would cause significant issues that hamper system scalability and stability. The RDAV center's Eden software package helps manage large numbers of concurrent serial jobs with high throughput for any such application. Here, the authors describe the motivation and technical nature of Eden and report representative use cases they've participated in during the past two years.
INDEX TERMS
Computer architecture, Career development, Program processors, High performance computing, Data analysis, Parallel processing, Operating systems,programming languages, Computer architecture, Career development, Program processors, High performance computing, Data analysis, Parallel processing, Operating systems, scientific computing, high-performance computing, scheduling, process management, operating systems, software, software engineering, multiprogramming, multiprocessing, multicore, concurrency, scripting languages
CITATION
Scott Simmerman, James Osborne, Jian Huang, "Eden: Simplified Management of Atypical High-Performance Computing Jobs", Computing in Science & Engineering, vol.15, no. 6, pp. 46-54, Nov.-Dec. 2013, doi:10.1109/MCSE.2012.92
REFERENCES
1. I. Raicu, I. Foster, and Y. Zhao, “Many-Task Computing for Grids and Supercomputers,” Proc. IEEE Workshop on Many-Task Computing on Grids and Supercomputers, IEEE, 2008; doi:10.1109/MTAGS.2008.4777912.
2. C. Engelmann, H.H. Ong, and S.L. Scott, “Middleware in Modern High Performance Computing System Architectures,” Proc. 7th Int'l Conf. Computational Science, Springer, 2007, pp. 784-791.
3. S.M. Kelly, R. Klundt, and J.H. Laros, “Shared Libraries on a Capability Class Computer,” Cray User Group 2011 Proc., Sandia, 2011; https://cfwebprod.sandia.gov/cfdocs/CCIM/ docsSAND2011-3455C-CUG-SharedLibrariesr.pdf .
4. S.J. Phillips, R.P. Anderson, and R.E. Schapire, “Maximum Entropy Modeling of Species Geographic Distributions,” Ecological Modeling, vol. 190, nos. 3–4, 2006, pp. 231-259.
5. D.B. Crawley et al., “EnergyPlus: Creating a New-Generation Building Energy Simulation Program,” Energy and Buildings, vol. 33, no. 4, 2001, pp. 319-331.
6. R. Buyya, D. Abramson, and J. Giddy, “Nimrod/G: An Architecture for a Resource Management and Scheduling System in a Global Computational Grid,” Proc. 4th Int'l Conf./Exhibition on High Performance Computing in the Asia-Pacific Region, IEEE, 2000, pp. 283-289.
7. M. Wilde et al., “Swift: A Language for Distributed Parallel Scripting,” J. Parallel Computing, vol. 37, no. 9, 2011, pp. 633-652.
8. Y. Zhao et al., “Swift: Fast, Reliable, Loosely Coupled Parallel Computation,” IEEE Congress on Services, IEEE, 2007, pp. 199-206, 2007.
116 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool