Topology for statistical modeling of petascale data.

Pascucci, Valerio (University of Utah, Salt Lake City, UT) ; Mascarenhas, Ajith Arthur ; Rusek, Korben (Texas A& ; M University, College Station, TX) ; Bennett, Janine Camille ; Levine, Joshua (University of Utah, Salt Lake City, UT) ; Pebay, Philippe Pierre ; Gyulassy, Attila (University of Utah, Salt Lake City, UT) ; Thompson, David C. ; Rojas, Joseph Maurice (Texas A& ; M University, College Station, TX)

Sandia National Laboratories

关键词: Statistics;    99 General And Miscellaneous//Mathematics, Computing, And Information Science;    Simulation;    Algorithms;    Statistical Models;   

DOI  :  10.2172/1022199
RP-ID  :  SAND2011-4612
RP-ID  :  AC04-94AL85000
RP-ID  :  1022199

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来源: UNT Digital Library

【 摘 要 】

This document presents current technical progress and dissemination of results for the Mathematics for Analysis of Petascale Data (MAPD) project titled 'Topology for Statistical Modeling of Petascale Data', funded by the Office of Science Advanced Scientific Computing Research (ASCR) Applied Math program. Many commonly used algorithms for mathematical analysis do not scale well enough to accommodate the size or complexity of petascale data produced by computational simulations. The primary goal of this project is thus to develop new mathematical tools that address both the petascale size and uncertain nature of current data. At a high level, our approach is based on the complementary techniques of combinatorial topology and statistical modeling. In particular, we use combinatorial topology to filter out spurious data that would otherwise skew statistical modeling techniques, and we employ advanced algorithms from algebraic statistics to efficiently find globally optimal fits to statistical models. This document summarizes the technical advances we have made to date that were made possible in whole or in part by MAPD funding. These technical contributions can be divided loosely into three categories: (1) advances in the field of combinatorial topology, (2) advances in statistical modeling, and (3) new integrated topological and statistical methods.