Scientific Big Data Visualization: a coupled tools approach

Abstract

We designed and implemented a parallel visualisation system for the analysis of large scale time-dependent particle type data. The particular challenge we address is how to analyse a high perfor- mance computation style dataset when a visual representation of the full set is not possible or useful, and one is only interested in finding and inspecting smaller subsets that fulfil certain complex criteria. We used Paraview as the user interface, which is a familiar tool for many HPC users, runs in parallel, and can be conveniently extended. We distributed the data in a supercomputing environment using the Hadoop file system. On top of it, we run Hive or Impala, and implemented a connection between Paraview and them that al- lows us to launch programmable SQL queries in the database di- rectly from within Paraview. The queries return a Paraview-native VTK object that fits directly into the Paraview pipeline. We find good scalability and response times. In the typical supercomputer environment (like the one we used for implementation) the queue and management system make it difficult to keep local data in be- tween sessions, which imposes a bottleneck in the data loading stage. This makes our system most useful when permanently in- stalled on a dedicated cluster.