Data Fusion at Scale in Astronomy

We have arrived in an era where we face a deluge of data streaming in from countless sources and across virtually all disciplines; This holds especially true for data intensive sciences such as astronomy where upcoming surveys such as the LSST are expected to collect tens of terabytes per night, upwards of 100 Petabytes in 10 years. The challenge is keeping up with these data rates and extracting meaningful information from them. We present a number of methods for combining and distilling vast astronomy datasets using GPUs. In particular we focus on cross-matching catalogs containing close to 0.5 Billion sources, optimally combining multi-epoch imagery and computationally extracting color from monochrome telescope images