A Case Study in Representing Scientific Applications ( GeoAc ) Using the Sparse Polyhedral Framework
Ravi Shankar · 2021
Tsunami detection and forecasting is a difficult problem that scientists are trying to tackle. Early path estimation and accurate prediction of the arrival time and size of a tsunami can save lives and help with impact assessment. Tsunami inducing earthquakes cause ground and sea-surface displacements that push up on the atmosphere. This atmospheric disturbance propagates upwards as an acoustic wave and eventually hits the ionosphere. IonoSeis is a software simulation package that leverages satellite-based ionospheric remote-sensing techniques to determine the epicenter of these earthquakes. The execution time of the ray-tracing component of IonoSeis prevents its use as a real-time modeling tool. A proposed solution is to replace this component with a newer ray-tracing package developed by Los Alamos National Laboratory called GeoAc and parallelize it. This research is a case study that uses the sparse polyhedral framework (SPF) to represent the operational GeoAc code and thereby drive the requirements for a SPF optimization framework that is being actively developed.