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Math @ Duke
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Publications [#367621] of Amanda Randles
Papers Published
- Puleri, DF; Roychowdhury, S; Balogh, P; Gounley, J; Draeger, EW; Ames, J; Adebiyi, A; Chidyagwai, S; Hernández, B; Lee, S; Moore, SV; Vetter, JS; Randles, A, High Performance Adaptive Physics Refinement to Enable Large-Scale Tracking of Cancer Cell Trajectory.,
Proceedings. IEEE International Conference on Cluster Computing, vol. 2022
(September, 2022),
pp. 230-242, ISBN 9781665498562 [doi]
(last updated on 2025/12/31)
Abstract: The ability to track simulated cancer cells through the circulatory system, important for developing a mechanistic understanding of metastatic spread, pushes the limits of today's supercomputers by requiring the simulation of large fluid volumes at cellular-scale resolution. To overcome this challenge, we introduce a new adaptive physics refinement (APR) method that captures cellular-scale interaction across large domains and leverages a hybrid CPU-GPU approach to maximize performance. Through algorithmic advances that integrate multi-physics and multi-resolution models, we establish a finely resolved window with explicitly modeled cells coupled to a coarsely resolved bulk fluid domain. In this work we present multiple validations of the APR framework by comparing against fully resolved fluid-structure interaction methods and employ techniques, such as latency hiding and maximizing memory bandwidth, to effectively utilize heterogeneous node architectures. Collectively, these computational developments and performance optimizations provide a robust and scalable framework to enable system-level simulations of cancer cell transport.
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