Some of the material in is restricted to members of the community. By logging in, you may be able to gain additional access to certain collections or items. If you have questions about access or logging in, please use the form on the Contact Page.
Molecular Dynamics (MD) is an important simulation technique with widespread use in computational chemistry, biology, and materials. An important limitation of MD is that the time step size is limited to around a femto (10-15) second. Consequently, a large number of iterations are required to simulate to realistic time spans. This is a major bottleneck in MD, and has been identified as an important challenge in computational biology and nano-materials. While parallelization has been effective in dealing with the computational effort that arises in simulating large physical systems (that is, having a large number of atoms), conventional parallelization is not effective in simulating small or moderate sized physical systems to long time spans. We recently introduced a new approach to parallelization, where data from prior simulations are used to parallelize a new computation along the time domain. We demonstrated its effectiveness in a nano-materials application, where this approach scaled to a larger number of processors than conventional parallelization. In this thesis, we parallelize a computational biology application – the AFM pulling of a protein – using this approach. The significance of this work arises in demonstrating the effectiveness of this technique in a soft-matter application, which is more challenging than the hard-matter applications considered earlier.
This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). The copyright in theses and dissertations completed at Florida State University is held by the students who author them.