Exploring Power Behaviors and Trade-offs of In-situ Data Analytics. Gamell, M., Rodero, I., Parashar, M., Bennett, J. C., Kolla, H., Chen, J., Bremer, P., Landge, A. G., Gyulassy, A., McCormick, P., Pakin, S., Pascucci, V., & Klasky, S. In Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, of SC '13, pages 77:1–77:12, New York, NY, USA, 2013. ACM.
Exploring Power Behaviors and Trade-offs of In-situ Data Analytics [pdf]Paper  doi  abstract   bibtex   
As scientific applications target exascale, challenges related to data and energy are becoming dominating concerns. For example, coupled simulation workflows are increasingly adopting in-situ data processing and analysis techniques to address costs and overheads due to data movement and I/O. However it is also critical to understand these overheads and associated trade-offs from an energy perspective. The goal of this paper is exploring data-related energy/performance trade-offs for end-to-end simulation workflows running at scale on current high-end computing systems. Specifically, this paper presents: (1) an analysis of the data-related behaviors of a combustion simulation workflow with an in-situ data analytics pipeline, running on the Titan system at ORNL; (2) a power model based on system power and data exchange patterns, which is empirically validated; and (3) the use of the model to characterize the energy behavior of the workflow and to explore energy/performance trade-offs on current as well as emerging systems.

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