Data-intensive management and analysis for scientifc simulations.
- CLS-CI
Scientific simulations can produce enormous amounts of data, making the analysis of results and management of files a difficult task for scientists. The simulation management and analysis system (Smaash) described here is designed to allow scientists to easily capture, store, organize, monitor, and analyze simulation results. The system is automatic, standardized, and secure. Smaash was built using open-source tools and modularized to be independent of the scientific simulation. The web-based front-end allows the scientist to easily interact with the data, and has proved its usefulness in improving the efficiency of a scientific team's workflow. High performance parallel computing allows scientists to solve complex physical problems through computer simulation. However, the massive amounts of data generated and the complex computing environment can create additional complications. A recent review by Ludaescher et al.(2009) describes how scientific workflows can assist scientists in extracting knowledge from these data-intensive operations by automating components within pipelines. Within the fusion community, Klasky et al.(2008) and colleagues have developed a system that handles the storage management, data movement, metadata generation and management, and a means to analyze the results. In response to scientists needs, a simulation management and analysis system (Smaash) was developed at the University of Chicago and Argonne National Laboratory (USA). Smaash provides an integrated way to monitor simulations and analyze computational results; catalog, store, and retrieve simulations; and prepare output for publications. The system is independent of the particular simulation code, accessible from many HPC and browser-based platforms, and built around open-source software tools. Data security and provenance is considered throughout. The analysis components are hidden behind a web-based front end, enabling scientists to focus on their results and not get bogged down by information overload.
- Research Organization:
- Argonne National Laboratory (ANL)
- Sponsoring Organization:
- SC
- DOE Contract Number:
- AC02-06CH11357
- OSTI ID:
- 1029097
- Report Number(s):
- ANL/MCS/CP-71213
- Country of Publication:
- United States
- Language:
- ENGLISH
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