Scott S. Hampton

ProtoMol, an object-oriented framework for prototyping novel algorithms for molecular dynamics

ProtoMol is a high-performance framework in C++ for rapid prototyping of novel algorithms for molecular dynamics and related applications. Its flexibility is achieved primarily through the use of inheritance and design patterns (object-oriented programming). Performance is obtained by using templates that enable generation of efficient code for sections critical to performance (generic programming). The framework encapsulates important optimizations that can be used by developers, such as parallelism in the force computation. Its design is based on domain analysis of numerical integrators for molecular dynamics (MD) and of fast solvers for the force computation, particularly due to electrostatic interactions. Several new and efficient algorithms are implemented in ProtoMol. Finally, it is shown that ProtoMols sequential performance is excellent when compared to a leading MD program, and that it scales well for moderate number of processors. Binaries and source codes for Windows, Linux, Solaris, IRIX, HP-UX, and AIX platforms are available under open source license at http://protomol.sourceforge.net.

Parallel multigrid summation for the N-body problem

An @Q(n) parallel multigrid summation method (MG) for the N-body problem is presented. The method was originally devised for vacuum boundary conditions. Here, it is extended to periodic boundary conditions and implemented in parallel using force decomposition and MPI. MG is based on a hierarchical decomposition of computational kernels on multiple grids. For low accuracy calculations, appropriate for molecular dynamics, a sequential implementation is as fast or faster than particle mesh Ewald (PME). Our parallel implementation is more scalable than PME. The method can be combined with multiple time stepping integrators to produce a powerful simulation protocol for simulation of biological molecules and other materials. The parallel implementation is tested on both a Linux cluster with Myrinet interconnect and a shared memory computer. It is available as open-source at http://protomol.sourceforge.net. An auxiliary tool allows the automatic selection of optimal parameters for MG, and is available at http://mdsimaid.cse.nd.edu.

MDSIMAID: Automatic parameter optimization in fast electrostatic algorithms

MDSIMAID is a recommender system that optimizes parallel Particle Mesh Ewald (PME) and both sequential and parallel multigrid (MG) summation fast electrostatic solvers. MDSIMAID optimizes the running time or parallel scalability of these methods within a given error tolerance. MDSIMAID performs a run time constrained search on the parameter space of each method starting from semiempirical performance models. Recommended parameters are presented to the user. MDSIMAIDs optimization of MG leads to configurations that are up to 14 times faster or 17 times more accurate than published recommendations. Optimization of PME can improve its parallel scalability, making it run twice as fast in parallel in our tests. MDSIMAID and its Python source code are accessible through a Web portal located at http://mdsimaid.cse.nd.edu.

Biomolecular Sampling: Algorithms, Test Molecules, and Metrics

We compare the effectiveness of different simulation sampling techniques by illustrating their application to united atom butane, alanine dipeptide, and a small solvated protein, BPTI. We introduce an optimization of the Shadow Hybrid Monte Carlo algorithm, a rigorous method that removes the bias of molecular dynamics. We also evaluate the ability of constant-temperature MD methods (based on Langevin and Nose-Hoover dynamics) to achieve uniform thermal equilibrium. Our results show the superiority of Langevin dynamics over Nose-Hoover dynamics to achieve thermal equilibrium. They also illustrate the inherent limitation of protocols that rely on sampling the microcanonical and canonical ensemble at only one temperature, and the importance of generalized ensemble approaches such as replica exchange that sample using different temperatures. Finally, we show how SHMC is able to remove bias and scale with timestep and system size. Presented, herein, are a set of sampling algorithms, test molecules, and metrics. We make the sampling methods discussed available via the open source and freely distributed ProtOmol molecular simulation framework.

Improved Sampling for Biological Molecules Using Shadow Hybrid Monte Carlo

Shadow Hybrid Monte Carlo (SHMC) is a new method for sampling the phase space of large biological molecules. It improves sampling by allowing larger time steps and system sizes in the molecular dynamics (MD) step of Hybrid Monte Carlo (HMC). This is achieved by sampling from high order approximations to the modified Hamiltonian, which is exactly integrated by a symplectic MD integrator. SHMC requires extra storage, modest computational overhead, and a reweighting step to obtain averages from the canonical ensemble. Numerical experiments are performed on biological molecules, ranging from a small peptide with 66 atoms to a large solvated protein with 14281 atoms. Experimentally, SHMC achieves an order magnitude speedup in sampling efficiency for medium sized proteins.