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 $z \sim0$ 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 128K cores of Blue Waters. We also demonstrate scaling up to 128K 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 128K cores is greater by a factor of 2. We also demonstrate strong scaling on up to 512K cores of Blue Waters for two large, uniform datasets with 12 and 24 billion particles.

中文翻译：

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聚类N体宇宙学模拟的自适应技术

ChaNGa是使用Charm ++实现的N体宇宙学模拟应用程序。在本文中，我们提出了ChaNGa的并行设计，并解决了由于聚类数据集的高动态范围而引起的许多挑战。我们提出了基于自适应技术的优化。我们评估ChaNGa在高度聚类的数据集上的性能：25 mpc体积的20亿个粒子实现的$ z \ sim0 $快照，矮星系的5200万个粒子多分辨率实现。对于25 Mpc的体积，我们在Blue Waters的多达128K内核上显示出强大的扩展能力。我们还演示了20亿个粒子模拟的多步运行最多可扩展到128K个核。尽管多步运行的扩展性不如单步运行，但128K内核的吞吐量却提高了2倍。