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DEPLOYMENT SCENARIOS OF PARALLELIZED CODE IN STOCHASTIC OPTIMIZATION

The benefit of using parallel hardware in real-time environments is obvious: For example, if it is necessary to solve some optimization task periodically in a narrow time window a parallelized optimization algorithm can possibly meet the time constraints. In case of deterministic algorithms the situation is clear. But if we use randomized algorithms some questions appear: As randomized algorithms must be run more than once to get a reliable solution we can execute the sequential code in parallel independently or we can execute the parallelized code simultaneously on the parallel hardware in a successive manner. Which approach is better? We analyze several scenarios analytically and offer conditions for deciding when to deploy the parallelized code and when not.