The accuracy of Conditional Random Fields (CRF) is
achieved at the cost of huge amount of computation to train
model. In this paper we designed the parallelized algorithm for
the Gradient Ascent based CRF training methods for biological
sequence alignment. Our contribution is mainly on two aspects: 1)
We flexibly parallelized the different iterative computation
patterns, and the according optimization methods are presented.
2) As for the Gibbs Sampling based training method, we designed
a way to automatically predict the iteration round, so that the
parallel algorithm could be run in a more efficient manner. In the
experiment, these parallel algorithms achieved valuable
accelerations comparing to the serial version.
