Design, Manufacture & Industrial Innovation Academic Cyber Infrastructure Investments

1. The basis for the systems structure, optimization and algorithms for cost-effective design, reliability, network capacity building and sharing, and the underlying fundamental science / engineering for grid applications The study of special structures to alleviate some of the computational burdenof very large scale modeling Cyber-tools for distributed and collaborative systems design, decision-based optimization, applications of utility theory and game theory to engineered systems and validation methods for designing under uncertainty. Virtual machine tools, distributed sensors and systems for manufacturing Distributed Modeling, Simulation, and Agent-based Optimization Design, Manufacture & Industrial Innovation Academic Cyber Infrastructure Investments Suvrajeet Sen, Program Director Operations Research and Service Enterprise Engineering DMII Academic Investment Areas FY02-03 Town Hall Meeting – October 29, 2003

2. 35.0% 30.0% 25.0% 20.0% 15.0% 10.0% 5.0% 0.0% Education and Community Building Functional Systems Engineering Physical Assets Research/Software Research/Physical Assets Domain-neutral Software Domain-specific Software Social, Industrial and Technological Dimensions DMII Research Cyberinfrastructure Investments in FY02 and FY03 ($60M+) % of DMII Research CI Investment in Each Category

3. DMII Vision of Cyberinfrastructure

4. Cyberplatform Operations Real-time resource allocation, job scheduling Cybertools Support for modeling and solver suites (e.g. NEOS), support for software implementation and benchmarking (COIN and MW). Interoperability. New middleware to allow heterogeneous modeling Applications to design, manufacture, and critical service infrastructure Use of Cyberplatforms for exchange of large scale datasets, models, and simulations Cyberinfrastructure and DMII

5. Cybertools to Support Modeling and Solver Suite Network Enabled Optimization Server (NEOS), Fourer, Moré (DMII Grant: 0322580) To establish large-scale optimization as an Internet resource, through investigation of current barriers to widespread adoption of new optimization technologies. We have established the validity of the optimization server concept, through worldwide use and in a prize-winning article. We have shown that widely-used algebraic modeling systems can be made to work as clients to a remote optimization server. We have established a prototype optimization server that makes available 35 varied solvers and attracts 2-3 thousand submissions per week. This facility will serve as a realistic test bed for investigation of difficult issues in standardizing problem representations, scheduling diverse submissions, choosing solvers, benchmarking and verifying results, and interfacing to computational grids. Making large-scale optimization a part of the worldwide software infrastructure that supports science and commerce, with benefits directly to practitioners and indirectly through improvement to the quality of optimization research and education.

6. Cyberdomain: Impact of Metamodel-Driven Visualization on DesignDr. Timothy W. Simpson, Dr. Mary Frecker, Dr. Russell Barton, and Dr. Ling RothrockThe Pennsylvania State University DMI-0084918 We are studying the impact of having fast, graphical design interfaces on design effectiveness, efficiency, and satisfaction during the design optimization process. . Our work has led to collaborations with Boeing and Lockheed Martin Space Systems to develop multi-dimensional visualization interfaces to help design engineers “shop” for promising design concepts.

7. Cyberdomain: Creating a Virtual Environment for Spatial Mechanism Design Dr. Judy M. Vance Pierre Larochelle Iowa State University Florida Inst of TechnologyDMI-9872604DMI-9816611 New analytical techniques for spatial mechanism design which fully utilize the capabilities of the virtual design environment have been generated and incorporated into the design software. VRSpatial, provides a variety of methods for selection and evaluation of a spatial mechanism. The Guide Map classifies the possible solutions by mechanism type and implements filtering algorithms that check for circuit and branch defects in order to help users identify suitable mechanism solutions.

8. More Cybertools for Software ImplementationMaster Worker (MW)Linderoth Facilitate implementation of master-worker, task-farming, and work-cycle algorithms on computational Grids and other parallel platforms. MW Toolkit has been used to solve numerical optimization problems of unprecedented size and complexity, including the famous nug30 quadratic assignment problem, and stochastic linear programs with 10,000,000 scenarios MW Toolkit implemented as middleware for computational Grids built from Condor, Globus, MPI, or PVM. Users fill in templates that define a task (unit of work) and the master’s response to a completed task. MW and underlying layers handle resource discovery, allocation, scheduling, and fault tolerance. MW aims to become de facto standard for master-worker, task-farming, work-cycle computations on Grids for science and engineering applications, with a particular emphasis on OR applications.

9. Research Objectives: • Significant Results: • Approach • Partners: • Broader Impact: More Cybertools for Software ImplementationComputational Infrastructure for Operations Research (COIN-OR) Saltzman et al. Open source software tools for including: • Very large-scale (i.e., grid) optimization framework • Interoperability support through standard APIs and data formats Thirteen projects (integrated and standalone) under active development. Active developer and user community with highly-regarded code in use in significant applications. Develop, solicit, and distribute open-source OR software, including a framework for highly scalable parallel solvers. Develop standards for interoperability, including APIs and model and instance data interchange formats. Provide infrastructure to support community development of OR software tools. Project initiated at IBM Research (2000) Researchers from academia and industry, including: • Clemson University • Frontline Systems • IBM Research • Konrad-Zuse-Zentrum Berlin • Lehigh University • Maximal Software • Mosek ApS • Simon Frazer • University of Arizona • University of Pittsburgh • et al. Speed R&D process through publication of state-of-the-art codes, utilities, etc Encourage software reuse rather than reinvention. Encourage open-source development and use in academia and industry.