Massively parallel cosmological simulations with ChaNGa

Cosmological simulators are an important component in the study of the formation of galaxies and large scale structures, and can help answer many important questions about the universe. Despite their utility, existing parallel simulators do not scale effectively on modern machines containing thousands of processors. In this paper we present ChaNGa, a recently released production simulator based on the CHARM++ infrastructure. To achieve scalable performance, ChaNGa employs various optimizations that maximize the overlap between computation and communication. We present experimental results of ChaNGa simulations on machines with thousands of processors, including the IBM Blue Gene/L and the Cray XT3. The paper goes on to highlight efforts toward even more efficient and scalable cosmological simulations. In particular, novel load balancing schemes that base decisions on certain characteristics of tree-based particle codes are discussed. Further, the multistepping capabilities of ChaNGa are presented, as are solutions to the load imbalance that such multiphase simulations face. We outline key requirements for an effective practical implementation and conclude by discussing preliminary results from simulations run with our multiphase load balancer.