Supercomputing Enabled Deployable Analytics for Disaster Response

Kaira Samuel, Jeremy Kepner, Michael Jones, Lauren Milechin, Vijay Gadepally, William Arcand, David Bestor, William Bergeron, Chansup Byun, Matthew Hubbell, Michael Houle, Anna Klein, Victor Lopez, Julie Mullen, Andrew Prout, Albert Reuther, Antonio Rosa, Sid Samsi, Charles Yee, Peter Michaleas

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

First responders and other forward deployed essential workers can benefit from advanced analytics. Limited network access and software security requirements prevent the usage of standard cloud based microservice analytic platforms that are typically used in industry. One solution is to precompute a wide range of analytics as files that can be used with standard preinstalled software that does not require network access or additional software and can run on a wide range of legacy hardware. In response to the COVID-19 pandemic, this approach was tested for providing geo-spatial census data to allow quick analysis of demographic data for better responding to emergencies. These data were processed using the MIT SuperCloud to create several thousand Google Earth and Microsoft Excel files representative of many advanced analytics. The fast mapping of census data using Google Earth and Microsoft Excel has the potential to give emergency responders a powerful tool to improve emergency preparedness. Our approach displays relevant census data (total population, population under 15, population over 65, median age) per census block, sorted by county, through a Microsoft Excel spreadsheet (xlsx file) and Google Earth map (kml file). The spreadsheet interface includes features that allow users to convert between different longitude and latitude coordinate units. For the Google Earth files, a variety of absolute and relative colors maps of population density have been explored to provide an intuitive and meaningful interface. Using several hundred cores on the MIT SuperCloud, new analytics can be generated in a few minutes.

Original languageEnglish
Title of host publication2021 IEEE High Performance Extreme Computing Conference, HPEC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665423694
DOIs
StatePublished - 2021
Externally publishedYes
Event2021 IEEE High Performance Extreme Computing Conference, HPEC 2021 - Virtual, Online, United States
Duration: Sep 20 2021Sep 24 2021

Publication series

Name2021 IEEE High Performance Extreme Computing Conference, HPEC 2021

Conference

Conference2021 IEEE High Performance Extreme Computing Conference, HPEC 2021
Country/TerritoryUnited States
CityVirtual, Online
Period09/20/2109/24/21

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