mD3DOCKxb: A Deep Parallel Optimized Software for Molecular Docking with Intel Xeon Phi Coprocessors
Qian Cheng, Shaoliang Peng, Yutong Lu, Weiliang Zhu, Zhijian Xu, Xinben Zhang · 2015
Molecular docking is a time consuming process, and it requires a substantial amount of computing power. D3DOCkxb was developed for investigating the effects of halogen bond in drug discovery by adding two precise score functions to Auto Dock. The docking accuracy of D3DOCkxb is better than Auto Dock, which can be attributed to a more complicated processing logic of D3DOCkxb. Consequently, it is an even more challenging task to do parallel optimization on D3DOCkxb. In this paper, we developed mD3DOCkxb, a MIC enabled version of D3DOCkxb, which utilizes Intel Xeon Phi, a Many-Integrated Core (MIC) accelerator, to boost the docking performance. We parallelized the Lamarckian Genetic Algorithm (LGA) in D3DOCKxb with OpenMP and port it to MIC with a number of optimization. And 12x to 18x speedup can be achieved, depending on the number of LGA iterations.