2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) (2016)
May 16, 2016 to May 19, 2016
The increasing gap between available compute power and I/O capabilities is resulting in simulation pipelines running on leadership computing facilities being reformulated. In particular, in-situ processing is complementing conventional post-process analysis, however, it can be performed by using the same compute resources as the simulation or using secondary dedicated resources. In this paper, we focus on three different in-situ analysis strategies, which use the same compute resources as the ongoing simulation but different data movement strategies. We evaluate the costs incurred by these strategies in terms of run time, scalability and power/energy consumption. Furthermore, we extrapolate power behavior to peta-scale and investigate different design choices through projections. Experimental evaluation at full machine scale on Titan supports that using fewer cores per node for in-situ analysis is the optimum choice in terms of scalability. Hence, further research effort should be devoted towards developing in-situ analysis techniques following this strategy in future high-end systems.
Computational modeling, Analytical models, Data models, Scalability, Feature extraction, Algorithm design and analysis, Load modeling
I. Rodero, M. Parashar, A. G. Landge, S. Kumar, V. Pascucci and P. Bremer, "Evaluation of In-Situ Analysis Strategies at Scale for Power Efficiency and Scalability," 2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid)(CCGRID), Cartagena, Colombia, 2016, pp. 156-164.