  We discuss the implementation of two distributed solvers of the random K-SAT
problem, based on some development of the recently introduced
survey-propagation (SP) algorithm. The first solver, called the "SP diffusion
algorithm", diffuses as dynamical information the maximum bias over the system,
so that variable nodes can decide to freeze in a self-organized way, each
variable making its decision on the basis of purely local information. The
second solver, called the "SP reinforcement algorithm", makes use of
time-dependent external forcing messages on each variable, which let the
variables get completely polarized in the direction of a solution at the end of
a single convergence. Both methods allow us to find a solution of the random
3-SAT problem in a range of parameters comparable with the best previously
described serialized solvers. The simulated time of convergence towards a
solution (if these solvers were implemented on a distributed device) grows as
log(N).
