Large scale experiment and optimization of a distributed stochastic control algorithm. Application to energy management problems

Asset management for the electricity industry leads to very large stochastic optimization problem. We explain in this article how to efficiently distribute the Bellman algorithm used, re-distributing data and computations at each time step, and we examine the parallelization of a simulation algorithm usually used after this optimization part. We focus on distributed architectures with shared memory multi-core nodes, and we design a multiparadigm parallel algorithm, implemented with both MPI and multithreading mechanisms. Then we lay emphasis on the serial optimizations carried out to achieve high performances both on a dual-core PC cluster and a Blue Gene/P IBM supercomputer with quad-core nodes. Finally, we introduce experimental results achieved on two large testbeds, running a 7-stocks and 10-state-variables benchmark, and we show the impact of multithreading and serial optimizations on our distributed application.