  We introduce a highly-parallelizable architecture for estimating parameters
of compact binary coalescence using gravitational-wave data and waveform
models. Using a spherical harmonic mode decomposition, the waveform is
expressed as a sum over modes that depend on the intrinsic parameters (e.g.
masses) with coefficients that depend on the observer dependent extrinsic
parameters (e.g. distance, sky position). The data is then prefiltered against
those modes, at fixed intrinsic parameters, enabling efficiently evaluation of
the likelihood for generic source positions and orientations, independent of
waveform length or generation time. We efficiently parallelize our intrinsic
space calculation by integrating over all extrinsic parameters using a Monte
Carlo integration strategy. Since the waveform generation and prefiltering
happens only once, the cost of integration dominates the procedure. Also, we
operate hierarchically, using information from existing gravitational-wave
searches to identify the regions of parameter space to emphasize in our
sampling. As proof of concept and verification of the result, we have
implemented this algorithm using standard time-domain waveforms, processing
each event in less than one hour on recent computing hardware. For most events
we evaluate the marginalized likelihood (evidence) with statistical errors of
less than about 5%, and even smaller in many cases. With a bounded runtime
independent of the waveform model starting frequency, a nearly-unchanged
strategy could estimate NS-NS parameters in the 2018 advanced LIGO era. Our
algorithm is usable with any noise curve and existing time-domain model at any
mass, including some waveforms which are computationally costly to evolve.
