Nonequilibrium umbrella sampling (NEUS) addresses the
challenging problem of rare events in biology and chemistry, such as the transition between folded and unfolder states of a protein. This is done through the application of an umbrella sampling technique for non-equilibrium systems that divides the phase space into regions based on their distance from equilibrium and traces the dynamic trajectory of the system in these regions with enhanced sampling.
Compared with traditional sampling methods, NEUS dramatically reduces the first passage time to observe extremely rare unfolding events by many orders of magnitude. The parallel NEUS algorithm must maintain a
large amount of data about the trajectories in each phase space region and the boundary regions between them. The distribution of data, communication between processes, and load balance are critical to the performance of the algorithm. In this talk, I'll present our analysis, implementation, and performance results on the BG/P system.
Improved data structures for one-sided access, the application of MPI one-sided communication and other optimizations to this application have yielded several-fold performance gains over the original application. The end result is a more portable, more robust and more scalable framework for performing enhanced sampling simulations in
biology and chemistry.