A Cloud Ecosystem for Data and Software Developed by SCOPED
Description:
Observational seismology favors distributed memory workflows and embarrassing parallelization, for which cloud computing has an optimal architecture. Cloud computing offers horizontal scalability for computing workflows suited for single-station analysis. The orchestration of software, data, and resources on the cloud requires some thinking about the design. We present the initial experimentation of the SCOPED project. The SCOPED software ecosystem uses a base container and software-specific containers built on top, forming a registry of containers deployable on cloud instances. The SCOPED data uses S3-like objects, using mSEED files for raw seismic data and tileDB (the new Earthscope Consortium Cloud store) for data products. We propose the first cloudstore for Distributed Acoustic Sensing data. We illustrate the workflows using canonical examples in observational seismology: ambient-noise seismology and machine-learning earthquake workflows.
Session: Geophysical Data Analysis in Cloud Computing Environments [Poster]
Type: Poster
Date: 4/18/2023
Presentation Time: 08:00 AM (local time)
Presenting Author: Carl Tape
Student Presenter: No
Invited Presentation: Yes
Authors
Marine Denolle
Corresponding Author
mdenolle@uw.edu
University of Washington
Yinzhi Wang
iwang@tacc.utexas.edu
Texas Advanced Computing Center
Carl Tape
Presenting Author
ctape@alaska.edu
University of Alaska Fairbanks
Yiyu Ni
niyiyu@uw.edu
University of Washington
Felix Waldhauser
felixw@ldeo.columbia.edu
Columbia University
Ebru Bozdag
bozdag@mines.edu
Colorado School of Mines
A Cloud Ecosystem for Data and Software Developed by SCOPED
Category
Geophysical Data Analysis in Cloud Computing Environments