Driving Scientific Applications by Data in Distributed Environments
Abstract
Traditional simulation-based applications for exploring a parameter space to understand a physical phenomenon or to optimize a design are rapidly overwhelmed by data volume when large numbers of simulations of different parameters are carried out. Optimizing reservoir management through simulation-based studies, in which large numbers of realizations are sought using detailed geologic descriptions, is an example of such applications. In this paper, we describe a software architecture to facilitate large scale simulation studies, involving ensembles of long-running simulations and analysis of vast volumes of output data. This architecture is built on top of two frameworks we have developed: IPARS and DataCutter. These frameworks make it possible to implement tools and applications to run large-scale simulations, and generate and investigate terabyte-scale datasets efficiently.
Document Details
- Document Type
- Technical Report
- Publication Date
- Feb 01, 2003
- Accession Number
- AD1001141
Entities
People
- Alan Sussman
- Christian Hansen
- Dennis Sessanna
- Don Stredney
- Joel Saltz
- Malgorzata Peszynska
- Mary Wheeler
- Michael Beynon
- Mike Gray
- Ryan Martino
- Shannon Hastings
- Sivaramakrishnan Narayanan
- Steve Langella
- Steven Bryant
- Tahsin Kurc
- Umit Catalyurek
Organizations
- Ohio State University