TY - GEN
T1 - Supercomputing Enabled Deployable Analytics for Disaster Response
AU - Samuel, Kaira
AU - Kepner, Jeremy
AU - Jones, Michael
AU - Milechin, Lauren
AU - Gadepally, Vijay
AU - Arcand, William
AU - Bestor, David
AU - Bergeron, William
AU - Byun, Chansup
AU - Hubbell, Matthew
AU - Houle, Michael
AU - Klein, Anna
AU - Lopez, Victor
AU - Mullen, Julie
AU - Prout, Andrew
AU - Reuther, Albert
AU - Rosa, Antonio
AU - Samsi, Sid
AU - Yee, Charles
AU - Michaleas, Peter
N1 - Funding Information:
This material is based upon work supported by the Assistant Secretary of Defense for Research and Engineering under Air Force Contract No. FA8702-15-D-0001, National Science Foundation CCF-1533644, and United States Air Force Research Laboratory Cooperative Agreement Number FA8750-19-2-1000. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Assistant Secretary of Defense for Research and Engineering, the National Science Foundation, or the United States Air Force. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85123474199&partnerID=8YFLogxK
U2 - 10.1109/HPEC49654.2021.9622808
DO - 10.1109/HPEC49654.2021.9622808
M3 - Conference contribution
AN - SCOPUS:85123474199
T3 - 2021 IEEE High Performance Extreme Computing Conference, HPEC 2021
BT - 2021 IEEE High Performance Extreme Computing Conference, HPEC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 20 September 2021 through 24 September 2021
ER -