The scale of functional magnetic resonance image data is rapidly increasing
as large multi-subject datasets are becoming widely available and
high-resolution scanners are adopted. The inherent low-dimensionality of the
information in this data has led neuroscientists to consider factor analysis
methods to extract and analyze the underlying brain activity. In this work, we
consider two recent multi-subject factor analysis methods: the Shared Response
Model and Hierarchical Topographic Factor Analysis. We perform analytical,
algorithmic, and code optimization to enable multi-node parallel
implementations to scale. Single-node improvements result in 99x and 1812x
speedups on these two methods, and enables the processing of larger datasets.
Our distributed implementations show strong scaling of 3.3x and 5.5x
respectively with 20 nodes on real datasets. We also demonstrate weak scaling
on a synthetic dataset with 1024 subjects, on up to 1024 nodes and 32,768
cores.

本研究比较了两种最近的多主体因子分析方法：Shared Response Model 和 Hierarchical Topographic Factor Analysis，对其进行了分析、算法和代码优化，以实现多节点并行应用的规模化，提供较高的速度市场，同时在真实数据集中表现出很好的强伸缩性和在合成数据集中表现出好的弱伸缩性。