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Scalable, High-Performance Forward Time Population Genetic Simulation

Putnam, Patrick P
http://orcid.org/0000-0001-7679-9355
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1522419645847035

Abstract Details

2018, PhD, University of Cincinnati, Engineering and Applied Science: Computer Science and Engineering.
Forward-time population genetic simulators are computational tools used in the study of population genetics. Simulations aim to evolve the genetic state of a population relative to a set of genetic models that reflect the processes that occur in nature under various configurations. Often, these simulations are limited to evolutionary scales that can be represented within the memory space and feasibly computed using a standard workstation computer. This presents a general challenge of how to represent the genetics of a population to enable evolutionary scenarios of sufficient scale to be performed on a memory constrained system. In addition, as the evolutionary scales increase so too does the computational time necessary to complete the simulation. This work considers the general problems of scale and performance as they relate to forward-time population genetic simulation. It explores the representation of a population from the perspective of a graph. Improved memory utilization and computational performance are achieved through the use of a binary adjacency matrix representation of the graph. This use of this representation is generally uncommon in forward-time population genetic simulation. Further improvements are made to the performance of the simulator through the use of parallel computation. This work considers a forward-time population genetic simulation from both a taskand a data- parallel perspective. Each of these perspectives present certain challenges and offer different levels of performance gains. The utilization of the binary adjacency matrix representation enables each of these parallel approaches to be achieved. Finally, although scale and performance improvements are enabled through the use of a binary adjacency matrix representation of the graph, it does have limits in forward-time population genetic simulation. These limits are related to the density of the graph. This work offers a situation where this representation would not be beneficial.
Philip Wilsey, Ph.D. (Committee Chair)
Fred Beyette, Ph.D. (Committee Member)
Yizong Cheng, Ph.D. (Committee Member)
Karen Davis, Ph.D. (Committee Member)
Ge Zhang, Ph.D. (Committee Member)
75 p.
Computer Science
Computer Simulation; Scalability; High Performance Computing; Forward Time Population Genetic Simulation

Recommended Citations

Citations

  • Putnam, P. P. (2018). Scalable, High-Performance Forward Time Population Genetic Simulation [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1522419645847035

    APA Style (7th edition)

  • Putnam, Patrick. Scalable, High-Performance Forward Time Population Genetic Simulation. 2018. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1522419645847035.

    MLA Style (8th edition)

  • Putnam, Patrick. "Scalable, High-Performance Forward Time Population Genetic Simulation." Doctoral dissertation, University of Cincinnati, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1522419645847035

    Chicago Manual of Style (17th edition)

ucin1522419645847035
280
© 2018, some rights reserved.Creative Commons License Scalable, High-Performance Forward Time Population Genetic Simulation by Patrick P Putnam is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Cincinnati and OhioLINK.