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Title: Enhancing the capabilities of computational chemistry using GPU technology
Author: Needham, Perri
Awarding Body: University of Manchester
Current Institution: University of Manchester
Date of Award: 2013
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Three key enhancements were made to a semiempirical molecular orbital program to develop a fast, accurate method of calculating chemical properties of large (> 1000 atom) molecular systems, through the use of quantum theory. In this thesis the key enhancements are presented which are: the implementation of a divide-and-conquer approach to a self-consistent field procedure, in an effort to improve capability; the use of the novel technology, GPU technology, to parallelize the divide-and-conquer self-consistent field procedure, in an effort to improve the speed; the implementation of a newly developed semiempirical model, the Polarized Molecular Orbital Model, in an effort to improve the accuracy. The development of a divide-and-conquer approach to the SCF (DC-SCF) procedure (enhancement 1) was carried out using saturated hydrocarbon chains whereby the saturated hydrocarbon chain is partitioned into small overlapping subsystems and the Roothaan equations solved for each subsystem. An investigation was carried out to find the optimal partitioning scheme for saturated hydrocarbon chains in order to minimize the loss of energy experienced from neglecting some of the interactions in the system whilst maintaining near linear scaling with system size. The DC-SCF procedure was shown to be accurate to 10-3 kcal mol-1 per atom whilst calculating the SCF-energy nearly 6 times faster than using the standard SCF procedure, for a 698-atom system. The development of a parallel DC-SCF procedure and Cartesian forces calculation for use on a GPU (enhancement 2), resulted in a hybrid CPU/GPU DC-SCF implementation that calculated the energy of a 1997-atom saturated hydrocarbon chain 21 times faster than the standard serial SCF implementation and a accelerated Cartesian forces calculation that performed 7 times faster for a saturated hydrocarbon chain of 1205-atoms, when accelerated using an NVidia Tesla C2050 GPU. The hybrid CPU/GPU algorithm made use of commercially accelerated linear algebra libraries, CULA and CUBLAS. A comparison was made between CULA’s accelerated eigensolver routine and the accelerated DC-eigensolver (developed in this research) and it was found that for saturated hydrocarbon chains of > 350 atoms, the accelerated DC-eigensolver performed around twice as fast as the accelerated CULA eigensolver. The implementation of the Polarized Molecular Orbital model (enhancement 3) was validated against published isomerization energies and benchmarked against the non-nitrogen containing complexes in the S66 database. The benchmark complexes were categorized according to dominant intermolecular interactions namely, hydrogen bonding, dispersion interactions and mixed interactions. After assessment it was found that the PMO model predicts interaction energies of complexes with a mixture of dispersive and electrostatic interactions to the highest accuracy (0.69 kcal mol-1 with respect to CCSD(T)/CBS). The dispersion correction within the PMO model was found to ‘overcorrect’ the dispersive contribution for most complexes tested. The outcome of this research is a semiempirical molecular orbital program that calculates the energy of a closed-shell saturated hydrocarbon chain of ~2000 atoms in under 4 minutes instead of 1.5 hours when using a PM3-Hamiltonian and can calculate interaction energies of systems exhibiting a mixture of electrostatic and dispersive interactions to an accuracy of within 1 kcal mol-1 (relative to high-level quantum methods). To demonstrate a suitable application for the enhanced SE-MO program, interaction energies of a series of PAHs with water, phenol and methanol have been investigated. The resultant program is suitable for use in calculating the energy and forces of large material and (in future) biological systems by a fast and accurate method that would be impractical or impossible to do without these enhancements.
Supervisor: Burton, Neil Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: gpu acceleration ; divide-and-conquer ; semiempirical