Interoperable Design of Extreme-scale Application Software (IDEAS)
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Project Vision
Advances in next-generation computational science and engineering (CSE) require the development of applications that can fully exploit emerging extreme-scale architectures for optimal performance and provide high-fidelity multiphysics and multiscale capabilities. To help address overwhelming complexity, the IDEAS family of projects focuses on improving scientific productivity by qualitatively improving developer productivity (positively impacting product quality, development time, and staffing resources) and software sustainability (reducing the cost of maintaining, sustaining, and evolving software capabilities in the future)—thereby enabling a fundamentally different attitude to creating and supporting CSE applications.
We are creating an extreme-scale scientific software development ecosystem composed of high-quality, reusable CSE software components and libraries; a collection of best practices, processes, and tools; and substantial outreach mechanisms for promoting and disseminating productivity improvements. We intend to improve CSE productivity by enabling better, faster and cheaper CSE application capabilities for extreme-scale computing.
Objectives
IDEAS projects address productivity concerns that are emerging from important trends in extreme-scale computing for science and engineering. In particular, the projects:
- Address a confluence of trends in hardware and increasing demands for predictive multiscale and multiphysics simulations, analysis, and design.
- Respond to a trend of continuous refactoring with efficient, agile software engineering methodologies and improved software design.
Projects
Find out more about specific projects in the IDEAS family.
Active
- IDEAS-ECP is supported by the DOE Exascale Computing Project to address issues of software productivity and sustainability in the ECP.
Past
- IDEAS-Classic was sponsored by the DOE Office of Science, Offices of Advanced Scientific Computing Research (ASCR) and Biological and Environmental Research (BER) to address issues of software productivity and sustainability in the Office of Science computational science and engineering community, with a particular emphasis on Terrestrial Ecosystem Modeling.