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Phoenix666 writes:

Researchers from Julia Computing, UC Berkeley, Intel, the National Energy Research Scientific Computing Center (NERSC), Lawrence Berkeley National Laboratory, and JuliaLabs@MIT have developed a new parallel computing method to dramatically scale up the process of cataloging astronomical objects. This major improvement leverages 8,192 Intel Xeon processors in Berkeley Lab's new Cori supercomputer and Julia, the high-performance, open-source scientific computing language to deliver a 225x increase in the speed of astronomical image analysis.

The code used for this analysis is called Celeste. It was developed at Berkeley Lab and uses statistical inference to mathematically locate and characterize light sources in the sky. When it was first released in 2015, Celeste was limited to single-node execution on at most hundreds of megabytes of astronomical images. In the case of the Sloan Digital Sky Survey (SDSS), which is the dataset used for this research, this analysis is conducted by identifying points of light in nearly 5 million images of approximately 12 megabytes each – a dataset of 55 terabytes.

Using the new parallel implementation, the research team dramatically increased the speed of its analysis by an estimated 225x. This enabled the processing of more than 20 thousand images, or 250 gigabytes – an increase of more than 3 orders of magnitude compared with previous iterations.

The original paper is available.

Original Submission