Multi-objective aware extraction of task-level parallelism using genetic algorithms

A large amount of research work has been done in the area of automatic parallelization for decades, resulting in a huge amount of tools, which should relieve the designer from the burden of manually parallelizing an application. Unfortunately, most of these tools are only optimizing the execution time by splitting up applications into concurrently executed tasks. In the domain of embedded devices, however, it is not sufficient to look only at this criterion. Since most of these devices are constraint-driven regarding execution time, energy consumption, heat dissipation and other objectives, a good trade-off has to be found to efficiently map applications to multiprocessor system on chip (MPSoC) devices. Therefore, we developed a fully automated multi-objective aware parallelization framework, which optimizes different objectives at the same time. The tool returns a Pareto-optimal front of solutions of the parallelized application to the designer, so that the solution with the best trade-off can be chosen.