Self-Adapting Numerical Software for Next Generation Applications

Abstract

The challenge for the development of next generation software is the successful management of the complex grid environment while delivering to the scientist the full power of flexible compositions of the available algorithmic alternatives. Self-Adapting Numerical Software (SANS) systems are intended to meet this significant challenge. A SANS system comprises intelligent next generation numerical software that domain scientists - with disparate levels of knowledge of algorithmic and programmatic complexities of the underlying numerical software - can use to easily express and efficiently solve their problem. The components of a SANS system are: • A SANS agent with: - An intelligent component that automates method selection based on data, algorithm and system attributes. - A system component that provides intelligent management of and access to the computational grid. - A history database that records relevant information generated by the intelligent component and maintains past performance data of the interaction (e.g., algorithmic, hardware specific, etc.) between SANS components. • A simple scripting language that allows a structured multilayered implementation of the SANS while ensuring portability and extensibility of the user interface and underlying libraries. • An XML/CCA-based vocabulary of metadata to describe behavioral properties of both data and algorithms. • System components, including a runtime adaptive scheduler, and prototype libraries that automate the process of architecture-dependent tuning to optimize performance on different platforms. A SANS system can dramatically improve the ability of computational scientists to model complex, interdisciplinary phenomena with maximum efficiency and a minimum of extra-domain expertise. SANS innovations (and their generalizations) will provide to the scientific and engineering community a dynamic computational environment in which the most effective library components are automatically selected based on the problem characteristics, data attributes, and the state of the grid.