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XtremWeb: a generic global computing system
Abstract
Global computing achieves high throughput computing by harvesting a very large number of unused computing resources connected to the Internet. This parallel computing model targets a parallel architecture defined by a very high number of nodes, poor communication performance and continuously varying resources. The unprecedented scale of the global computing architecture paradigm requires us to revisit many basic issues related to parallel architecture programming models, performance models, and class of applications or algorithms suitable for this architecture. XtremWeb is an experimental global computing platform dedicated to provide a tool for such studies. The paper presents the design of XtremWeb. Two essential features of this design are multi-applications and high-performance. Accepting multiple applications allows institutions or enterprises to set up their own global computing applications or experiments. High-performance is ensured by scalability, fault tolerance, efficient scheduling and a large base of volunteer PCs. We also present an implementation of the first global application running on XtremWeb.
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Citations
Cited by
BOINC: A System for Public-Resource Computing and Storage
Anderson, D. P. (2005) · Fifth IEEE/ACM International Workshop on Grid Computing · doi:10.1109/grid.2004.14 · Cited by 1,924
Scientific Cloud Computing: Early Definition and Experience
Wang, L., Tao, J., Kunze, M., Castellanos, A. C., Kramer, D., & Karl, W. (2008) · 2008 10th IEEE International Conference on High Performance Computing and Communications · doi:10.1109/hpcc.2008.38 · Cited by 532
Metadata
- Authors
- Fedak, G., Germain, C., Neri, V., & Cappello, F.
- Venue
- Proceedings First IEEE/ACM International Symposium on Cluster Computing and the Grid
- Publisher
- IEEE Comput. Soc
- Published
- 2002-11-13
- Pages
- 582-587
- OpenAlex
- W2119772483
- Cited by
- 359
- References
- 0
- FWCI
- 12.79
- Type
- conference paper
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
