  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.
