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Title: Parallel auto-correlative statistics with VTK.

Abstract

This report summarizes existing statistical engines in VTK and presents both the serial and parallel auto-correlative statistics engines. It is a sequel to [PT08, BPRT09b, PT09, BPT09, PT10] which studied the parallel descriptive, correlative, multi-correlative, principal component analysis, contingency, k-means, and order statistics engines. The ease of use of the new parallel auto-correlative statistics engine is illustrated by the means of C++ code snippets and algorithm verification is provided. This report justifies the design of the statistics engines with parallel scalability in mind, and provides scalability and speed-up analysis results for the autocorrelative statistics engine.

Authors:
;
Publication Date:
Research Org.:
Sandia National Lab. (SNL-CA), Livermore, CA (United States); Kitware, France,
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1095960
Report Number(s):
SAND2013-3435
463505
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English

Citation Formats

Pebay, Philippe Pierre, and Bennett, Janine Camille. Parallel auto-correlative statistics with VTK.. United States: N. p., 2013. Web. doi:10.2172/1095960.
Pebay, Philippe Pierre, & Bennett, Janine Camille. Parallel auto-correlative statistics with VTK.. United States. doi:10.2172/1095960.
Pebay, Philippe Pierre, and Bennett, Janine Camille. Thu . "Parallel auto-correlative statistics with VTK.". United States. doi:10.2172/1095960. https://www.osti.gov/servlets/purl/1095960.
@article{osti_1095960,
title = {Parallel auto-correlative statistics with VTK.},
author = {Pebay, Philippe Pierre and Bennett, Janine Camille},
abstractNote = {This report summarizes existing statistical engines in VTK and presents both the serial and parallel auto-correlative statistics engines. It is a sequel to [PT08, BPRT09b, PT09, BPT09, PT10] which studied the parallel descriptive, correlative, multi-correlative, principal component analysis, contingency, k-means, and order statistics engines. The ease of use of the new parallel auto-correlative statistics engine is illustrated by the means of C++ code snippets and algorithm verification is provided. This report justifies the design of the statistics engines with parallel scalability in mind, and provides scalability and speed-up analysis results for the autocorrelative statistics engine.},
doi = {10.2172/1095960},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2013},
month = {8}
}

Technical Report:

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