In this paper we apply regenerative simulation and distributed computing to study the energy efficiency of a supercomputer with speed scaling. We use generalized semi-Markov processes to simulate the supercomputer in steady state, and perform exhaustive search of the optimal speed scaling policy in a small-scale heterogeneous model where the per-class amount of work has a heavy-tailed distribution. The preliminary simulation results are reported, which demonstrate the capabilities of the software packages used.