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.
['Marc Gamell', 'Ivan Rodero', 'Manish Parashar', 'Janine C. Bennett', 'Hemanth Kolla', 'Jacqueline H. Chen', 'Peer Timo Bremer', 'Aaditya G. Landge', 'Attila Gyulassy', 'Patrick S. McCormick', 'Scott Pakin', 'Valerio Pascucci', 'Scott Klasky']
Exploring power behaviors and trade-offs of in-situ data analytics