The SML language for structured modeling: levels 1 and 2
Operations Research
Load balancing in the parallel optimization of block-angular linear programs
Mathematical Programming: Series A and B
Meta-modeling concepts and tools for model management: a systems approach
Management Science
Using MPI: portable parallel programming with the message-passing interface
Using MPI: portable parallel programming with the message-passing interface
A worldwide flock of Condors: load sharing among workstation clusters
Future Generation Computer Systems - Special issue: resource management in distributed systems
Distributed nested decomposition of staircase linear programs
ACM Transactions on Mathematical Software (TOMS)
Effective distribution of object-oriented applications
Communications of the ACM
Future trends in model management systems: parallel and distributed extensions
Decision Support Systems
NEOS and Condor: solving optimization problems over the Internet
ACM Transactions on Mathematical Software (TOMS)
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Decomposing Linear Programs for Parallel Solution
PARA '95 Proceedings of the Second International Workshop on Applied Parallel Computing, Computations in Physics, Chemistry and Engineering Science
ODE: a tool for distributing object-oriented applications
Information and Management
The Grid 2: Blueprint for a New Computing Infrastructure
The Grid 2: Blueprint for a New Computing Infrastructure
The Anatomy of the Grid: Enabling Scalable Virtual Organizations
International Journal of High Performance Computing Applications
An Approach to Distribution of Object-Oriented Applications in Loosely Coupled Networks
Journal of Management Information Systems
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Recent research on model management systems (MMS) recognizes the importance of considering potential algorithmic performance in the selection of an appropriate model to solve a real-world problem. Model selection, as typically viewed in the literature however, is the process of selecting from among alternative model classes, rather than from alternative mathematical representations of the same model class. In this paper, we take up this subtler aspect of model selection, and provide tangible evidence that shows how just changing the representation of a model can have a dramatic impact on algorithmic performance. Using problem decomposition and distributed processing, we conduct a series of computational experiments to study the interrelationships between model representation, computing capacity, and algorithmic performance. We discuss potential implications of our results for improving MMS design and address a key prerequisite for the enhanced design, by proposing and validating an approach for solution time prediction.