Adaptive techniques for clustered N-body cosmological simulations

Abstract

ChaNGa is an N-body cosmology simulation application implemented using Charm++ . In this paper, we present the parallel design of ChaNGa and address many challenges arising due to the high dynamic ranges of clustered datasets. We propose optimizations based on adaptive techniques. We evaluate the performance of ChaNGa on highly clustered datasets: a $z \sim0$ z ∼ 0 snapshot of a 2 billion particle realization of a 25 Mpc volume, and a 52 million particle multi-resolution realization of a dwarf galaxy. For the 25 Mpc volume, we show strong scaling on up to 128 K cores of Blue Waters. We also demonstrate scaling up to 128 K cores of a multi-stepping run of the 2 billion particle simulation. While the scaling of the multi-stepping run is not as good as single stepping, the throughput at 128 K cores is greater by a factor of 2. We also demonstrate strong scaling on up to 512 K cores of Blue Waters for two large, uniform datasets with 12 and 24 billion particles.