Learning an Astronomical Catalog of the Visible Universe through Scalable Bayesian Inference
This work addresses the computational bottleneck in processing large-scale astronomical datasets, enabling faster catalog generation for astronomers.
The authors tackled the problem of scaling Bayesian inference for astronomical catalog creation by developing a parallel version of Celeste, achieving efficient scaling on up to 8192 cores with state-of-the-art scientific results.
Celeste is a procedure for inferring astronomical catalogs that attains state-of-the-art scientific results. To date, Celeste has been scaled to at most hundreds of megabytes of astronomical images: Bayesian posterior inference is notoriously demanding computationally. In this paper, we report on a scalable, parallel version of Celeste, suitable for learning catalogs from modern large-scale astronomical datasets. Our algorithmic innovations include a fast numerical optimization routine for Bayesian posterior inference and a statistically efficient scheme for decomposing astronomical optimization problems into subproblems. Our scalable implementation is written entirely in Julia, a new high-level dynamic programming language designed for scientific and numerical computing. We use Julia's high-level constructs for shared and distributed memory parallelism, and demonstrate effective load balancing and efficient scaling on up to 8192 Xeon cores on the NERSC Cori supercomputer.