  The importance of algorithm portfolio techniques for SAT has long been noted,
and a number of very successful systems have been devised, including the most
successful one --- SATzilla. However, all these systems are quite complex (to
understand, reimplement, or modify). In this paper we propose a new algorithm
portfolio for SAT that is extremely simple, but in the same time so efficient
that it outperforms SATzilla. For a new SAT instance to be solved, our
portfolio finds its k-nearest neighbors from the training set and invokes a
solver that performs the best at those instances. The main distinguishing
feature of our algorithm portfolio is the locality of the selection procedure
--- the selection of a SAT solver is based only on few instances similar to the
input one.
