skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Performance Understanding and Analysis for Exascale Data Management Workflows

Abstract

The goal of the Exascale Data Management Workflows project is to create tools to support techniques for in-situ data analysis and data management. Online data management co-running with simulations accelerates the scientific processes being carried out, provides rapid and timely scientific insights, and can help avoid unnecessary and scientifically invalid simulation computations, particularly when combined with or able to utilize data from experimental instrument and/or from past simulation runs. The University of Oregon team is contributing to the performance instrumentation and analysis aspect of this project. Our focus has been on efficient performance monitoring of data management at scale.

Authors:
 [1]
  1. Univ. of Oregon, Eugene, OR (United States)
Publication Date:
Research Org.:
Univ. of Oregon, Eugene, OR (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
OSTI Identifier:
1546804
Report Number(s):
DOE-OREGON-12381
SN10019
DOE Contract Number:  
SC0012381
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
99 GENERAL AND MISCELLANEOUS; 97 MATHEMATICS AND COMPUTING; high performance computing; performance measurement; performance monitoring; monitoring and analytics; TAU; SOS; WOWMON

Citation Formats

Malony, Allen. Performance Understanding and Analysis for Exascale Data Management Workflows. United States: N. p., 2019. Web. doi:10.2172/1546804.
Malony, Allen. Performance Understanding and Analysis for Exascale Data Management Workflows. United States. doi:10.2172/1546804.
Malony, Allen. Mon . "Performance Understanding and Analysis for Exascale Data Management Workflows". United States. doi:10.2172/1546804. https://www.osti.gov/servlets/purl/1546804.
@article{osti_1546804,
title = {Performance Understanding and Analysis for Exascale Data Management Workflows},
author = {Malony, Allen},
abstractNote = {The goal of the Exascale Data Management Workflows project is to create tools to support techniques for in-situ data analysis and data management. Online data management co-running with simulations accelerates the scientific processes being carried out, provides rapid and timely scientific insights, and can help avoid unnecessary and scientifically invalid simulation computations, particularly when combined with or able to utilize data from experimental instrument and/or from past simulation runs. The University of Oregon team is contributing to the performance instrumentation and analysis aspect of this project. Our focus has been on efficient performance monitoring of data management at scale.},
doi = {10.2172/1546804},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {8}
}