  The utilization of statistical machine translation (SMT) has grown enormously
over the last decade, many using open-source software developed by the NLP
community. As commercial use has increased, there is need for software that is
optimized for commercial requirements, in particular, fast phrase-based
decoding and more efficient utilization of modern multicore servers.
  In this paper we re-examine the major components of phrase-based decoding and
decoder implementation with particular emphasis on speed and scalability on
multicore machines. The result is a drop-in replacement for the Moses decoder
which is up to fifteen times faster and scales monotonically with the number of
cores.
