Projects per year
Abstract
Bayesian inference is the workhorse of gravitationalwave astronomy, for example, determining the mass and spins of merging black holes, revealing the neutron star equation of state, and unveiling the population properties of compact binaries. The science enabled by these inferences comes with a computational cost that can limit the questions we are able to answer. This cost is expected to grow. As detectors improve, the detection rate will go up, allowing less time to analyze each event. Improvement in lowfrequency sensitivity will yield longer signals, increasing the number of computations per event. The growing number of entries in the transient catalog will drive up the cost of population studies. While Bayesian inference calculations are not entirely parallelizable, key components are embarrassingly parallel: calculating the gravitational waveform and evaluating the likelihood function. Graphical processor units (GPUs) are adept at such parallel calculations. We report on progress porting gravitationalwave inference calculations to GPUs. Using a single code  which takes advantage of GPU architecture if it is available  we compare computation times using modern GPUs (NVIDIA P100) and CPUs (Intel Gold 6140). We demonstrate speedups of ∼50× for compact binary coalescence gravitational waveform generation and likelihood evaluation, and more than 100× for population inference within the lifetime of current detectors. Further improvement is likely with continued development. Our pythonbased code is publicly available and can be used without familiarity with the parallel computing platform, CUDA.
Original language  English 

Article number  043030 
Number of pages  7 
Journal  Physical Review D 
Volume  100 
Issue number  4 
DOIs  
Publication status  Published  28 Aug 2019 
Projects
 1 Active

ARC Centre of Excellence for Gravitational Wave Discovery
Bailes, M., McClelland, D. E., Levin, Y., Blair, D. G., Scott, S. M., Ottaway, D. J., Melatos, A., Veitch, P. J., Wen, L., Shaddock, D. A., Slagmolen, B. J. J., Zhao, C., Evans, R. J., Ju, L., Galloway, D., Thrane, E., Hurley, J., Coward, D. M., Cooke, J., Couch, W., Hobbs, G. B., Reitze, D., Rowan, S., Cai, R., Adhikari, R. X., Danzmann, K., Mavalvala, N., Kulkarni, S. R., Kramer, M., Branchesi, M., Gehrels, N., Weinstein, A. J. R., Steeghs, D., Bock, D. & Lasky, P.
Monash University – Internal University Contribution, Monash University – Internal Department Contribution
1/01/17 → 31/03/24
Project: Research