Boosting performance of a Statistical Machine Translation system using dynamic parallelism

• New Statistical Machine Translation (SMT) system based on Moses toolkit.
• Translation jobs are processed in parallel using a different number of cores.
• The level of parallelism changes dynamically according to the load of the server.
• An autotuning module allows the system to adapt to any hardware platform.
• Important reductions in the translation times were observed for different scenarios.

In this work we introduce a new Statistical Machine Translation (SMT) system whose main objective is to reduce the translation times exploiting efficiently the computing power of the current processors and servers. Our system processes each individual job in parallel using different number of cores in such a way that the level of parallelism for each job changes dynamically according to the load of the translation server. In addition, the system is able to adapt to the particularities of any hardware platform used as server thanks to an autotuning module. An exhaustive performance evaluation considering different scenarios and hardware configurations demonstrates the benefits and flexibility of our proposal.