Decision-Theoretic Design Space Exploration of Multiprocessor Platforms

This paper presents an efficient technique to perform design space exploration of a multiprocessor platform that minimizes the number of simulations needed to identify a Pareto curve with metrics like energy and delay. Instead of using semi-random search algorithms (like simulated annealing, tabu search, genetic algorithms, etc.), we use the domain knowledge derived from the platform architecture to set-up the exploration as a discrete-space Markov decision process. The system walks the design space changing its parameters, performing simulations only when probabilistic information becomes insufficient for a decision. A learning algorithm updates the probabilities of decision outcomes as simulations are performed. The proposed technique has been tested with two multimedia industrial applications, namely the ffmpeg transcoder and the parallel pigz compression algorithm. Results show that the exploration can be performed with 5% of the simulations necessary for the most used algorithms (Pareto simulated annealing, nondominated sorting genetic algorithm, etc.), increasing the exploration speed by more than one order of magnitude