The Julia application that achieved this milestone is called Celeste. It was developed by a team of astronomers, physicists, computer engineers and statisticians from UC Berkeley, Lawrence Berkeley National Laboratory, National Energy Research Scientific Computing Center (NERSC), Intel, Julia Computing and the Julia Lab at MIT.
Celeste uses the Sloan Digital Sky Survey (SDSS), a dataset of astronomical images from the Apache Point Observatory in New Mexico that includes every visible object from over 35% of the sky - hundreds of millions of stars and galaxies. Light from the most distant of these galaxies has been traveling for billions of years and lets us see how the universe appeared in the distant past.
Since SDSS data collection began in 1998, the process of cataloging these stars and galaxies was painstaking and laborious.
So the Celeste team developed a new parallel computing method to process the entire SDSS dataset. Celeste is written entirely in Julia, and the Celeste team loaded an aggregate of 178 terabytes of image data to produce the most accurate catalogue of 188 million astronomical objects in just 14.6 minutes with state-of-the-art point and uncertainty estimates.
Celeste achieved peak performance of 1.54 petaflops using 1.3 million threads on 9,300 Knights Landing (KNL) nodes of the Cori supercomputer at NERSC - a performance improvement of 1,000x in single-threaded execution.
The Celeste research team is already looking to new challenges. For example, the Large Synoptic Survey Telescope (LSST), scheduled to begin operation in 2019, is 14 times larger than the Apache Point telescope and will produce 15 terabytes of images every night. This means that every few days, the LSST will produce more visual data than the Apache Point telescope has produced in 20 years. With Julia and the Cori supercomputer, the Celeste team can analyze and catalog every object in those nightly images in as little as 5 minutes.
The Celeste team is also working to:
The Celeste project is a shining example of: