Scalable Data Parallel Approaches to Anomaly Detection in Climate Data using Gaussian Processes

Abstract

Anomaly detection on large scale spatio-temporal data such as climate data is a challenging task depending on the spatial and temporal resolution and autocorrelation of the data. When considering global gridded daily temperature data, the number of locations and the length of time period considered makes anomaly detection a big data problem. Gaussian Process (GP) Learning is a method that is well-suited to identify the complex spatial and temporal autocorrelation properties of spatio-temporal data. One of the primary challenges with using GP is the computational complexity associated with inverting a covariance matrix. This is further compounded when considering data on a national/global scale and performing anomaly detection using such methods often requires dedicated high performance computing platforms. In this paper, we describe a purely temporal scalable anomaly detection technique for gridded temperature data based on GP Learning that ignore the spatial autocorrelation between neighboring grids and perform anomaly detection on each of the grids in parallel, thereby reducing the execution time. We introduce three methods: a standalone data parallel approach using a single GPU, a distributed memory version on multi-node clusters using MPI, and a mixed parallel approach using multiple GPUs. In comparison to a sequential approach, they are 17.2x, 47.1x, and 88.9x faster, respectively.