Open AccessAnalyzing the I/O Performance of Post-Hoc Visualization of Huge Simulation Datasets on the K Computer
Author(s) -
Eduardo C. Inacio,
Jorji aka,
Kenji Ono,
Mário A. R. Dantas
Publication year - 2017
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/wscad.2017.246
Subject(s) - visualization , computer science , pipeline (software) , supercomputer , set (abstract data type) , data visualization , interactive visual analysis , data science , data set , data mining , computational science , parallel computing , operating system , artificial intelligence , programming language
As computational science simulations produce ever increasing volumes of data, executing part or even the entire visualization pipeline in the supercomputer side becomes more a requirement than an option. Given the uniqueness of the high performance K computer architecture, the HIVE visualization framework was developed, focusing on meeting visualization and data analysis demands of scientists and engineers. In this paper, we present an analysis on the input/output (I/O) performance of post-hoc visualization. The contribution of this research work is characterized by an analysis of a set of empirical study cases considering huge simulation datasets using HIVE on the K computer. Results from the experimental effort, using a dataset produced by a real-world global climate simulation, provide a differentiated knowledge on the impact of dataset partitioning parameters in the I/O performance of large-scale visualization systems, and highlight challenges and opportunities for performance optimizations.