  The Spinning Effective One Body-Numerical Relativity (SEOBNR) series of
gravitational wave approximants are among the best available for Advanced LIGO
data analysis. Unfortunately, SEOBNR codes as they currently exist within
LALSuite are generally too slow to be directly useful for standard Markov-Chain
Monte Carlo-based parameter estimation (PE). Reduced-Order Models (ROMs) of
SEOBNR have been developed for this purpose, but there is no known way to make
ROMs of the full eight-dimensional intrinsic parameter space more efficient for
PE than the SEOBNR codes directly. So as a proof of principle, we have sped up
the original LALSuite SEOBNRv2 approximant code, which models waveforms from
aligned-spin systems, by nearly 300x. Our optimized code shortens the timescale
for conducting PE with this approximant to months, assuming a purely serial
analysis, so that even modest parallelization combined with our optimized code
will make running the full PE pipeline with SEOBNR codes directly a realistic
possibility. A number of our SEOBNRv2 optimizations have already been applied
to SEOBNRv3, a new approximant capable of modeling sources with all eight
(precessing) intrinsic degrees of freedom. We anticipate that once all of our
optimizations have been applied to SEOBNRv3, a similar speed-up may be
achieved.
