Accelerating parameter inference with graphics processing units

D. Wysocki, R. O’Shaughnessy, Jacob Lange, and Yao-Lung L. Fang
Phys. Rev. D 99, 084026 – Published 16 April 2019

Abstract

Gravitational wave Bayesian parameter inference involves repeated comparisons of gravitational wave data to generic candidate predictions. Even with algorithmically efficient methods such as RIFT or reduced-order quadrature, the time needed to perform these calculations and the overall computational cost can be significant compared to the minutes to hours needed to achieve the goals of low-latency multimessenger astronomy. By translating some elements of the RIFT algorithm to operate on graphics processing units, we demonstrate substantial performance improvements, enabling dramatically reduced overall cost and latency.

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  • Received 14 February 2019

DOI:https://doi.org/10.1103/PhysRevD.99.084026

© 2019 American Physical Society

Physics Subject Headings (PhySH)

Gravitation, Cosmology & Astrophysics

Authors & Affiliations

D. Wysocki, R. O’Shaughnessy, and Jacob Lange

  • Center for Computational Relativity and Gravitation, Rochester Institute of Technology, Rochester, New York 14623, USA

Yao-Lung L. Fang

  • Computational Science Initiative, Brookhaven National Laboratory, Upton, New York 11973, USA and National Synchrotron Light Source II, Brookhaven National Laboratory, Upton, New York 11973, USA

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Issue

Vol. 99, Iss. 8 — 15 April 2019

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