Abstract
We are developing the Delta framework that aims to tackle big-data challenges specific to fusion energy sciences. Delta can be used to connect fusion experiments to remote supercomputers. Streaming measurements to distributed compute resources allows to automatically perform high-dimensional data analysis on a cadence that exceeds experimental schedules. Making data analysis results available before the next experiments allows scientists to make more informed decisions about configuration of upcoming experiments. Here we describe how Delta uses database and virtualization facilities, as well as high-performance computing, at the National Energy Research Compute Center to offer a vertically integrated near real-Time data analysis and visualization. We also report on ongoing efforts to port the data analysis part of Delta to graphical processing units, which show a reduction of the analysis wall-Time for a benchmark workflow by about 35% when compared to a serial implementation.
| Original language | English |
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| Title of host publication | Proceedings of UrgentHPC 2020 |
| Subtitle of host publication | 2020 International Workshops on Urgent and Interactive HPC, Held in conjunction with SC 2020: The International Conference for High Performance Computing, Networking, Storage and Analysis |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 55-63 |
| Number of pages | 9 |
| ISBN (Electronic) | 9781665422741 |
| DOIs | |
| State | Published - Nov 2020 |
| Event | 2020 IEEE/ACM International Workshops on Urgent and Interactive HPC, UrgentHPC 2020 - Virtual, Atlanta, United States Duration: Nov 13 2020 → … |
Publication series
| Name | Proceedings of UrgentHPC 2020: 2020 International Workshops on Urgent and Interactive HPC, Held in conjunction with SC 2020: The International Conference for High Performance Computing, Networking, Storage and Analysis |
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Conference
| Conference | 2020 IEEE/ACM International Workshops on Urgent and Interactive HPC, UrgentHPC 2020 |
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| Country/Territory | United States |
| City | Virtual, Atlanta |
| Period | 11/13/20 → … |
Funding
ACKNOWLEDGEMENTS The authors are pleased to acknowledge that the work reported on in this paper was substantially performed using the Princeton Research Computing resources at Princeton University which is consortium of groups led by the Princeton Institute for Computational Science and Engineering (PICSciE) and Office of Information Technology’s Research Computing. This research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility operated under Contract No. DE-AC02-05CH11231.