Developing new parallelization techniques for emerging HPC architectures and life science applications

Parallelization of chemistry and drug discovery codes to leverage emerging High-Performance Computing (HPC) architectures is a difficult task. However, it is a task that is required for one to be able to simulate biologically important molecular systems which are not accessible with current technology. In addition to parallelizing existing chemistry and drug discovery codes, it is important to explore at the same time new methodologies that address limitations in methods currently being used for molecular simulations. In this study we combine speed and accuracy by parallelizing a new promising explicit polarization (X-Pol) method, which is a method that addresses several limitations in today´s methodologies. The explicit polarization (X-Pol) method, also called the X-Pol potential, provides a method for treating both bonded and nonbonded interactions using electronic structure theory. Bonded interactions are treated by an iterative self-consistent field method, and nonbonded interactions are treated by electronic embedding. In this approach partial charges are obtained using electronic structure methods applied to the individual fragments. When the method is applied to a protein, the fragments are defined as peptide units. In our research we intend to use of peptide fragments as defined in the X-Pol method as the basic units for parallelization. The peptide fragments are mapped onto a HPC hybrid architecture where shared-memory processors are interconnected by a customized or standard network. In our method a pre-selected number of fragments are parallelized via shared-memory algorithms, while message-passing algorithms are used across processors for neighboring fragments.