2026-09-18T18:42:01Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1445702022-08-30T03:02:09Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Schardl, Tao B.
Rosa, Isabel
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2022-08-29T15:56:32Z
2022-08-29T15:56:32Z
2022-05
2022-05-27T16:19:10.110Z
https://hdl.handle.net/1721.1/144570
The molecular dynamics method, used by scientists across the fields of physics, materials science, and biology, is an increasingly popular way to simulate particle interactions. Current implementations of molecular dynamics simulators can verify macromolecular structures, examine atomic-level phenomena that cannot be observed directly, and predict the behavior of unstudied proteins. The existing implementations, however, rely on inefficient directional message-passing algorithms on graph neural networks. This thesis presents a novel approach for the optimization of these algorithms using a stencil-like technique. The stencil-based algorithm, called StencilMD, provides both the benefits of parallelization and improved cache locality. The results show that StencilMD successfully reduces the amount of time required to run a molecular dynamics simulation by as much as 28.57% with a corresponding 26.92% decrease in cache misses.
M.Eng.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright MIT
http://rightsstatements.org/page/InC-EDU/1.0/
Performance Engineering of Directional Message-Passing Algorithms Through a Stencil-Based Approach for Applications in Molecular Dynamics
Thesis
application/pdf
Master
Master of Engineering in Electrical Engineering and Computer Science
Publication
unknown
unknown
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<Abstract>The molecular dynamics method, used by scientists across the fields of physics, materials science, and biology, is an increasingly popular way to simulate particle interactions. Current implementations of molecular dynamics simulators can verify macromolecular structures, examine atomic-level phenomena that cannot be observed directly, and predict the behavior of unstudied proteins. The existing implementations, however, rely on inefficient directional message-passing algorithms on graph neural networks. This thesis presents a novel approach for the optimization of these algorithms using a stencil-like technique. The stencil-based algorithm, called StencilMD, provides both the benefits of parallelization and improved cache locality. The results show that StencilMD successfully reduces the amount of time required to run a molecular dynamics simulation by as much as 28.57% with a corresponding 26.92% decrease in cache misses.</Abstract>
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