Parallelization and performance characterization of protein 3D structure prediction of Rosetta

Wenlong Li, Tao Wang, Eric Q. Li, David Baker, Li Jin, Steven Ge, Yurong Chen, Yimin Zhang

Abstract: The prediction of protein 3D structure has become a hot research area in the post-genome era, through which people can understand a protein's function in health and disease, explore ways to control its actions and assist drug design. Many protein structure prediction approaches have been proposed in past decades. Among them, Rosetta is one of the best systems. However, the huge time complexity of Rosetta, e.g. a few days to predict a protein, limits its wide use in practice. To accelerate the prediction of protein 3D structure in Rosetta, this paper presents three different approaches, i.e., non-interactive, periodic interactive and asynchronous dynamic interactive scheme, to parallelize Rosetta. The asynchronous interactive scheme, with the adaptation of dynamic solution interaction, outperforms the other two, delivering much faster convergence speed and better solution quality. Detailed measurements and performance analysis also indicate that parallel Rosetta with asynchronous dynamic interactive scheme scales well