Zenith seminar: Ji Liu “Efficient Uncertainty Analysis of Very Big Seismic Simulation Data ” 6 dec. 2017

Efficient Uncertainty Analysis of Very Big Seismic Simulation Data

Ji Liu

Zenith Postdoc

Wednesday 6 December at 11h

Room: 02/124, Bat 5

Abstract:

In recent years, big  simulation data is commonly generated from specific models,  in  different applications domains (astronomy, bioinformatics social networks, etc). In general, the simulation data corresponds to meshes that represent for instance a seismic soil area. It is of much importance to analyze the uncertainty of the simulation data in order to safely identify geological or seismic phenomenons, e.g. seismic faults. In order to analyze the uncertainty,  a  Probability Density Function (PDF) of each point in the mesh is computed to be analyzed.  However, this may be very time consuming (from several hours to even months) using a baseline approach based on parallel processing frameworks such as Spark. In this paper, we propose new solutions to efficiently compute and  analyze the uncertainty of very big simulation data using Spark. Our solutions use an original distributed architecture design. We propose three general approaches: data aggregation, machine learning  prediction and fast processing. We validate our approaches by extensive experimentations using big data ranging from hundreds of GB to several TB. The experimental results show that our approach  scales up very well and reduce the execution time by a factor of 33 (in the order of seconds or minutes) compared with a baseline approach.

This work is part of the  HPC4E European project, joint work with LNCC, Brazil co-authored with  N. Moreno, E. Pacitti, F. Porto and P. Valduriez