  Detecting continuous nanohertz gravitational waves (GWs) generated by
individual close binaries of supermassive black holes (CB-SMBHs) is one of the
primary objectives of pulsar timing arrays (PTAs). The detection sensitivity is
slated to increase significantly as the number of well-timed millisecond
pulsars will increase by more than an order of magnitude with the advent of
next-generation radio telescopes. Currently, the Bayesian analysis pipeline
using parallel tempering Markov chain Monte Carlo has been applied in multiple
studies for CB-SMBH searches, but it may be challenged by the high
dimensionality of the parameter space for future large-scale PTAs. One solution
is to reduce the dimensionality by maximizing or marginalizing over
uninformative parameters semi-analytically, but it is not clear whether this
approach can be extended to more complex signal models without making overly
simplified assumptions. Recently, the method of diffusive nested (DNest)
sampling shown the capability of coping with high dimensionality and
multimodality effectively in Bayesian analysis. In this paper, we apply DNest
to search for continuous GWs in simulated pulsar timing residuals and find that
it performs well in terms of accuracy, robustness, and efficiency for a PTA
including $\mathcal{O}(10^2)$ pulsars. DNest also allows a simultaneous search
of multiple sources elegantly, which demonstrates its scalability and general
applicability. Our results show that it is convenient and also high beneficial
to include DNest in current toolboxes of PTA analysis.
