Urgent Computing for Operational Storm Surge Forecasting in Saint-Petersburg

AbstractThe accurate forecasting of storm surges and decision support for gates maneuvering is an important issue in Saint-Petersburg. The evolution of the numerical hydrodynamic models, hardware performance and computer technologies allow to make Flood Warning System (FWS) in Saint-Petersburg more reliable and appropriate to the real needs. This article describes the key solutions of the development and the present operational set-up of FWS with emphasis on computational issues and decision support on the basis of urgent computing paradigm. It includes a brief description data-assimilation techniques, such as Kalman filtering, the probabilistic real-data forecasting model, forecast quality control, distributed computing of different scenarios and decision support for gates maneuvering

High Performance Computations for Decision Support in Critical Situations: Introduction to the Third Workshop on Urgent Computing

AbstractThis paper is the preface to the Third Workshop on Urgent Computing.The Urgent Computing workshops have been traditionally embedded in frame of International Conference of Computational Science (ICCS) since 2012. They are aimed to develop a dialogue on the present and future ofresearch and applications associated with the large-scale computations for decision support in critical situations. The key workshop topics in 2014 are: methods and principles of urgent computing, middleware, platforms and infrastructures, simulation-based decision support for complex systems control, interactive visualization and virtual reality for decision support in emergency situations, domain-area applications to emergency situations, including natural and man-made disasters, e.g.transportation problems, epidemics, criminal acts, etc

Distributed simulation of city inundation by coupled surface and subsurface porous flow for urban flood decision support system

We present a decision support system for flood early warning and disaster management. It includes the models for data-driven meteorological predictions, for simulation of atmospheric pressure, wind, long sea waves and seiches; a module for optimization of flood barrier gates operation; models for stability assessment of levees and embankments, for simulation of city inundation dynamics and citizens evacuation scenarios. The novelty of this paper is a coupled distributed simulation of surface and subsurface flows that can predict inundation of low-lying inland zones far from the submerged waterfront areas, as observed in St. Petersburg city during the floods. All the models are wrapped as software services in the CLAVIRE platform for urgent computing, which provides workflow management and resource orchestration.Comment: Pre-print submitted to the 2013 International Conference on Computational Scienc

Ontological Formalization for Workflow-based Computational Experiments

AbstractWorkflow-based computational experiment is a widespread way to organize distributed simulations. But the lack of IT experience and skills is the critical issue which scientists usually face with. By this paper we describe the reasoning capabilities, which are obtained from the proposed hierarchical structure for expert's knowledge formalization. The contribution of this paper is the ontological representation of a structure, which make end-users to deal with domain models compiled of fine-grained domain and infrastructural entities in order to generate an executable workflow as a result. A task of forecasting of storm surges and decision support for gates maneuvering is presented a use-case of the paper

Evaluating and Enabling Scalable High Performance Computing Workloads on Commercial Clouds

Performance, usability, and accessibility are critical components of high performance computing (HPC). Usability and performance are especially important to academic researchers as they generally have little time to learn a new technology and demand a certain type of performance in order to ensure the quality and quantity of their research results. We have observed that while not all workloads run well in the cloud, some workloads perform well. We have also observed that although commercial cloud adoption by industry has been growing at a rapid pace, its use by academic researchers has not grown as quickly. We aim to help close this gap and enable researchers to utilize the commercial cloud more efficiently and effectively. We present our results on architecting and benchmarking an HPC environment on Amazon Web Services (AWS) where we observe that there are particular types of applications that are and are not suited for the commercial cloud. Then, we present our results on architecting and building a provisioning and workflow management tool (PAW), where we developed an application that enables a user to launch an HPC environment in the cloud, execute a customizable workflow, and after the workflow has completed delete the HPC environment automatically. We then present our results on the scalability of PAW and the commercial cloud for compute intensive workloads by deploying a 1.1 million vCPU cluster. We then discuss our research into the feasibility of utilizing commercial cloud infrastructure to help tackle the large spikes and data-intensive characteristics of Transportation Cyberphysical Systems (TCPS) workloads. Then, we present our research in utilizing the commercial cloud for urgent HPC applications by deploying a 1.5 million vCPU cluster to process 211TB of traffic video data to be utilized by first responders during an evacuation situation. Lastly, we present the contributions and conclusions drawn from this work