Title :
Scalable parallel building blocks for custom data analysis
Author :
Peterka, T. ; Ross, R. ; Gyulassy, A. ; Pascucci, V. ; Kendall, W. ; Han-Wei Shen ; Teng-Yok Lee ; Chaudhuri, A.
Author_Institution :
Argonne Nat. Lab., Argonne, IL, USA
Abstract :
We present a set of building blocks that provide scalable data movement capability to computational scientists and visualization researchers for writing their own parallel analysis. The set includes scalable tools for domain decomposition, process assignment, parallel I/O, global reduction, and local neighborhood communicationtasks that are common across many analysis applications. The global reduction is performed with a new algorithm, described in this paper, that efficiently merges blocks of analysis results into a smaller number of larger blocks. The merging is configurable in the number of blocks that are reduced in each round, the number of rounds, and the total number of resulting blocks. We highlight the use of our library in two analysis applications: parallel streamline generation and parallel Morse-Smale topological analysis. The first case uses an existing local neighborhood communication algorithm, whereas the latter uses the new merge algorithm.
Keywords :
data analysis; data visualisation; merging; parallel programming; custom data analysis; data visualization; domain decomposition; local neighborhood communication; merge algorithm; parallel I/O; parallel Morse-Smale topological analysis; parallel analysis; parallel programming; parallel streamline generation; process assignment; scalable tools; Algorithm design and analysis; Computational modeling; Data models; Data structures; Libraries; Merging; Sorting;
Conference_Titel :
Large Data Analysis and Visualization (LDAV), 2011 IEEE Symposium on
Conference_Location :
Providence, Rl
Print_ISBN :
978-1-4673-0156-5
DOI :
10.1109/LDAV.2011.6092324