Distributed, Numerically Stable Distance and Covariance Computation with MPI for Extremely Large Datasets

Daniel Peralta, Y. Saeys
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引用次数: 2

Abstract

The current explosion of data, which is impacting many different areas, is especially noticeable in biomedical research thanks to the development of new technologies that are able to capture high-dimensional and high-resolution data at the single-cell scale. Processing such data in an interpretable way often requires the computation of pairwise dissimilarity measures between the multiple features of the data, a task that can be very difficult to tackle when the dataset is large enough, and which is prone to numerical instability. In this paper we propose a distributed framework to efficiently compute dissimilarity matrices in arbitrarily large datasets in a numerically robust way. It implements a combination of the pairwise and two-pass algorithms for computing the variance, in order to maintain the numerical robustness of the former while reducing its overhead. The proposal is parallelizable both across multiple computers and multiple cores, maximizing the performance while maintaining the benefits of memory locality. The proposal is tested on a real use case: a dataset generated from high-content screening images composed by a billion individual cells and 786 features. The results showed linear scalability with respect to the size of the dataset and close to linear speedup.
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用MPI计算超大数据集的分布、数值稳定距离和协方差
当前的数据爆炸正在影响许多不同的领域,由于能够在单细胞尺度上捕获高维和高分辨率数据的新技术的发展,在生物医学研究中尤为明显。以可解释的方式处理这些数据通常需要计算数据的多个特征之间的两两不相似度量,当数据集足够大时,这一任务可能很难处理,并且容易出现数值不稳定性。在本文中,我们提出了一个分布式框架,以一种数字鲁棒的方式有效地计算任意大数据集中的不相似矩阵。为了保持前者的数值鲁棒性,同时减少其开销,它实现了两两算法和两步算法的组合来计算方差。该建议可以跨多台计算机和多核并行,在保持内存局部性优势的同时最大限度地提高性能。该建议在一个真实的用例上进行了测试:一个由10亿个单个细胞和786个特征组成的高内容筛选图像生成的数据集。结果显示了相对于数据集大小的线性可扩展性和接近线性的加速。
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