Parallel distributed-memory simplex for large-scale stochastic LP problems

Contributed talk at CSC14: The Sixth SIAM Workshop on Combinatorial Scientific Computing, Lyon, 21st July 2014.

J. A. J. Hall


generally solved using the dual revised simplex method. Despite its potential value, there has been relatively little research into the development of implementations of the revised simplex method which exploit parallel computing environments. Hitherto these efforts have met with very limited success, in particular for general large sparse LP problems. This talk will discuss three recent approaches. The first two exploit data and task pallelism when solving general LP problems and achieve meaningful performance improvement relative to Clp, the leading open-source serial simplex solver. The third approach exploits the inherent data parallelism resulting from the structure of stochastic LP problems. Results obtained on a large cluster and supercomputer using a distributed-memory implementation are presented for stochastic LP problems with up to 108 variables and constraints.

PDF CSC14.pdf


Parallel distributed-memory simplex for large-scale stochastic LP problems
M. Lubin, J. A. J. Hall, C. G. Petra and M. Anitescu
Technical Report ERGO-12-005.
Computational Optimization and Applications 55(3), 571-596, 2013. DOI: 10.1007/s10589-013-9542-y