Adaptive Techniques for Clustered N-Body Cosmological Simulations

TL;DR

This paper discusses adaptive techniques in the parallel design of ChaNGa, an N-body cosmology simulation, enabling efficient scaling to hundreds of thousands of cores for large, clustered datasets.

Contribution

It introduces adaptive optimization strategies for ChaNGa that improve scalability and performance on large supercomputers for highly clustered cosmological simulations.

Findings

Strong scaling up to 512K cores for 12-24 billion particles

Effective handling of highly clustered datasets on 128K cores

Demonstrated scalability on Blue Waters supercomputer

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 focus on optimizations based on adaptive techniques for scaling to more than 128K cores. We demonstrate strong scaling on up to 512K cores of Blue Waters evolving 12 and 24 billion particles. We also show strong scaling of highly clustered datasets on up to 128K cores.