Efficient maintenance and update of nonbonded lists in macromolecular simulations
Paschalidis, Ioannis Ch.
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CitationRezaul Chowdhury, Dmitri Beglov, Mohammad Moghadasi, Ioannis Ch Paschalidis, Pirooz Vakili, Sandor Vajda, Chandrajit Bajaj, Dima Kozakov. 2014. "Efficient Maintenance and Update of Nonbonded Lists in Macromolecular Simulations." Journal of Chemical Theory and Computation, Volume 10, Issue 10, pp. 4449 - 4454.
Molecular mechanics and dynamics simulations use distance based cutoff approximations for faster computation of pairwise van der Waals and electrostatic energy terms. These approximations traditionally use a precalculated and periodically updated list of interacting atom pairs, known as the “nonbonded neighborhood lists” or nblists, in order to reduce the overhead of finding atom pairs that are within distance cutoff. The size of nblists grows linearly with the number of atoms in the system and superlinearly with the distance cutoff, and as a result, they require significant amount of memory for large molecular systems. The high space usage leads to poor cache performance, which slows computation for large distance cutoffs. Also, the high cost of updates means that one cannot afford to keep the data structure always synchronized with the configuration of the molecules when efficiency is at stake. We propose a dynamic octree data structure for implicit maintenance of nblists using space linear in the number of atoms but independent of the distance cutoff. The list can be updated very efficiently as the coordinates of atoms change during the simulation. Unlike explicit nblists, a single octree works for all distance cutoffs. In addition, octree is a cache-friendly data structure, and hence, it is less prone to cache miss slowdowns on modern memory hierarchies than nblists. Octrees use almost 2 orders of magnitude less memory, which is crucial for simulation of large systems, and while they are comparable in performance to nblists when the distance cutoff is small, they outperform nblists for larger systems and large cutoffs. Our tests show that octree implementation is approximately 1.5 times faster in practical use case scenarios as compared to nblists.
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