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Title: HPC Analytics Support. Requirements for Uncertainty Quantification Benchmarks

Abstract

This report outlines techniques for extending benchmark generation products so they support uncertainty quantification by benchmarked systems. We describe how uncertainty quantification requirements can be presented to candidate analytical tools supporting SPARQL. We describe benchmark data sets for evaluating uncertainty quantification, as well as an approach for using our benchmark generator to produce data sets for generating benchmark data sets.

Authors:
 [1];  [1];  [1]
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1225163
Report Number(s):
PNNL-24435
400470000
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
SPARQL; RDF; High Performance Computing; benchmark; Uncertainty Quantification

Citation Formats

Paulson, Patrick R., Purohit, Sumit, and Rodriguez, Luke R. HPC Analytics Support. Requirements for Uncertainty Quantification Benchmarks. United States: N. p., 2015. Web. doi:10.2172/1225163.
Paulson, Patrick R., Purohit, Sumit, & Rodriguez, Luke R. HPC Analytics Support. Requirements for Uncertainty Quantification Benchmarks. United States. doi:10.2172/1225163.
Paulson, Patrick R., Purohit, Sumit, and Rodriguez, Luke R. Fri . "HPC Analytics Support. Requirements for Uncertainty Quantification Benchmarks". United States. doi:10.2172/1225163. https://www.osti.gov/servlets/purl/1225163.
@article{osti_1225163,
title = {HPC Analytics Support. Requirements for Uncertainty Quantification Benchmarks},
author = {Paulson, Patrick R. and Purohit, Sumit and Rodriguez, Luke R.},
abstractNote = {This report outlines techniques for extending benchmark generation products so they support uncertainty quantification by benchmarked systems. We describe how uncertainty quantification requirements can be presented to candidate analytical tools supporting SPARQL. We describe benchmark data sets for evaluating uncertainty quantification, as well as an approach for using our benchmark generator to produce data sets for generating benchmark data sets.},
doi = {10.2172/1225163},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2015},
month = {5}
}

Technical Report:

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