SC17 Denver, CO

P33: Massively Parallel Evolutionary Computation for Empowering Electoral Reform: Quantifying Gerrymandering via Multi-objective Optimization and Statistical Analysis


Authors: Wendy K. Cho (National Center for Supercomputing Applications, University of Illinois), Yan Liu (National Center for Supercomputing Applications, University of Illinois)

Abstract: Important insights into redistricting can be gained by formulating and analyzing the problem within a large-scale optimization framework. Redistricting is an application of the set-partitioning problem that is NP-hard. We design a hybrid metaheuristic as the base search algorithm. With our grant on the Blue Waters supercomputer, we extend our algorithm to the high-performance-computing realm by using MPI to implement an asynchronous processor communication framework. We experimentally demonstrate the effectiveness of our algorithm to utilize multiple processors and to scale to 131,072 processors. The massive computing power allows us to extract new substantive insights that closely mesh with the framework that the Supreme Court has elucidated for electoral reform.
Award: Best Poster Finalist (BP): no

Poster: pdf
Two-page extended abstract: pdf


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