Kiran Mantripragada

Enabling high-resolution forecasting of severe weather and flooding events in Rio de Janeiro

Safe operation of many cities is affected by relative extremes in weather conditions. With precipitation events, local topography and weather influence water runoff and infiltration, which directly affect flooding. Hence, the availability of highly focused predictions has the potential to mitigate the impact of severe weather on a city. Often, such information is simply unavailable. The initial step to address this gap is the application of state-of-the-art weather models at an urban scale calibrated to address this mismatch. The generation of operational forecasts at such a scale for the Rio de Janeiro metropolitan area suggests a horizontal resolution of approximately 1 km and a vertical resolution in the lower boundary layer of tens of meters. Forecasting impacts from storm-driven flooding events requires the development of a coupled hydrological model that operates at a street scale with resolution of approximately 1 m, capturing local terrain effects and simulating surface flow and water accumulation, especially for overland flow and ponding depth. This coupled approach has enabled operational prediction of storm impacts on local infrastructure, as well as measurement of the model error associated with such forecasts.

An SLA-Based Advisor for Placement of HPC Jobs on Hybrid Clouds

Several scientific and industry applications require High Performance Computing (HPC) resources to process and/or simulate complex models. Not long ago, companies, research institutes, and universities used to acquire and maintain on-premise computer clusters; but, recently, cloud computing has emerged as an alternative for a subset of HPC applications. This poses a challenge to end-users, who have to decide where to run their jobs: on local clusters or burst to a remote cloud service provider. While current research on HPC cloud has focused on comparing performance of on-premise clusters against cloud resources, we build on top of existing efforts and introduce an advisory service to help users make this decision considering the trade-offs of resource costs, performance, and availability on hybrid clouds. We evaluated our service using a real test-bed with a seismic processing application based on Full Waveform Inversion; a technique used by geophysicists in the oil & gas industry and earthquake prediction. We also discuss how the advisor can be used for other applications and highlight the main lessons learned constructing this service to reduce costs and turnaround times.