  While a fully-coherent all-sky search is known to be optimal for detecting
gravitational wave signals from compact binary coalescences, its high
computational cost has limited current searches to less sensitive
coincidence-based schemes. Following up on previous work that has demonstrated
the effectiveness of Particle Swarm Optimization in reducing the computational
cost of this search, we present an implementation that achieves near real-time
computational speed. This is achieved by combining the search efficiency of PSO
with a significantly revised and optimized numerical implementation of the
underlying mathematical formalism along with additional multi-threaded
parallelization layers in a distributed computing framework. For a network of
four second-generation detectors with $60$~min data from each, the runtime of
the implementation presented here ranges between $\approx 1.4$ to $\approx 0.5$
times the data duration for network signal-to-noise ratios (SNRs) of $\gtrsim
10$ and $\gtrsim 12$, respectively. The reduced runtimes are obtained with
small to negligible losses in detection sensitivity: for a false alarm rate of
$\simeq 1$~event per year in Gaussian stationary noise, the loss in detection
probability is $\leq 5\%$ and $\leq 2\%$ for SNRs of $10$ and $12$,
respectively. Using the fast implementation, we are able to quantify
frequentist errors in parameter estimation for signals in the double neutron
star mass range using a large number of simulated data realizations. A clear
dependence of parameter estimation errors and detection sensitivity on the
condition number of the network antenna pattern matrix is revealed. Combined
with previous work, this paper securely establishes the effectiveness of
PSO-based fully-coherent all-sky search across the entire binary inspiral mass
range that is relevant to ground-based detectors.
