Combining in-situ and in-transit processing to enable extreme-scale scientific analysis

Janine C. Bennett, Hasan Abbasi, Peer-Timo Bremer, Ray Grout, Attila Gyulassy, Tong Jin, Scott Klasky, Hemanth Kolla, Manish Parashar, Valerio Pascucci, Philippe Pebay, David Thompson (+3 others)
2012 2012 International Conference for High Performance Computing, Networking, Storage and Analysis  
With the onset of extreme-scale computing, I/O constraints make it increasingly difficult for scientists to save a sufficient amount of raw simulation data to persistent storage. One potential solution is to change the data analysis pipeline from a post-process centric to a concurrent approach based on either in-situ or in-transit processing. In this context computations are considered in-situ if they utilize the primary compute resources, while in-transit processing refers to offloading
more » ... tions to a set of secondary resources using asynchronous data transfers. In this paper we explore the design and implementation of three common analysis techniques typically performed on large-scale scientific simulations: topological analysis, descriptive statistics, and visualization. We summarize algorithmic developments, describe a resource scheduling system to coordinate the execution of various analysis workflows, and discuss our implementation using the DataSpaces and ADIOS frameworks that support efficient data movement between in-situ and in-transit computations. We demonstrate the efficiency of our lightweight, flexible framework by deploying it on the Jaguar XK6 to analyze data generated by S3D, a massively parallel turbulent combustion code. Our framework allows scientists dealing with the data deluge at extreme scale to perform analyses at increased temporal resolutions, mitigate I/O costs, and significantly improve the time to insight.
doi:10.1109/sc.2012.31 dblp:conf/sc/BennettABGGJKKPPPTYZC12 fatcat:f3wyhi5khzgmfphpdpx7skvyoq