Accelerating parameter inference with graphics processing units
Journal Article
·
· Physical Review D
- Rochester Inst. of Technology, Rochester, NY (United States)
- Brookhaven National Lab. (BNL), Upton, NY (United States). National Synchrotron Light Source II (NSLS-II)
Gravitational wave Bayesian parameter inference involves repeated comparisons of GW data to generic candidate predictions. Even with algorithmically efficient methods like RIFT or reduced-order quadrature, the time needed to perform these calculations and 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 (GPU), we demonstrate substantial performance improvements, enabling dramatically reduced overall cost and latency.
- Research Organization:
- Brookhaven National Lab. (BNL), Upton, NY (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (SC-21); USDOE
- Grant/Contract Number:
- SC0012704; 17-029; 19-002
- OSTI ID:
- 1524547
- Alternate ID(s):
- OSTI ID: 1507226
- Report Number(s):
- BNL-211723-2019-JAAM; PRVDAQ
- Journal Information:
- Physical Review D, Vol. 99, Issue 8; ISSN 2470-0010
- Publisher:
- American Physical Society (APS)Copyright Statement
- Country of Publication:
- United States
- Language:
- English
Cited by: 30 works
Citation information provided by
Web of Science
Web of Science
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Higher order gravitational-wave modes with likelihood reweighting
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Parallelized Inference for Gravitational-Wave Astronomy | text | January 2019 |
Higher order gravitational-wave modes with likelihood reweighting | text | January 2019 |
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