High Performance Adaptive Physics Refinement to Enable Large-Scale Tracking of Cancer Cell Trajectory
Document Type
Conference Proceeding
Publication Date
1-1-2022
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.
Identifier
85140875114 (Scopus)
ISBN
[9781665498562]
Publication Title
Proceedings IEEE International Conference on Cluster Computing Iccc
External Full Text Location
https://doi.org/10.1109/CLUSTER51413.2022.00036
ISSN
15525244
First Page
230
Last Page
242
Volume
2022-September
Grant
1943036
Fund Ref
National Science Foundation
Recommended Citation
Puleri, Daniel F.; Roychowdhury, Sayan; Balogh, Peter; Gounley, John; Draeger, Erik W.; Ames, Jeff; Adebiyi, Adebayo; Chidyagwai, Simbarashe; Hernandez, Benjamin; Lee, Seyong; Moore, Shirley V.; Vetter, Jeffrey S.; and Randles, Amanda, "High Performance Adaptive Physics Refinement to Enable Large-Scale Tracking of Cancer Cell Trajectory" (2022). Faculty Publications. 3554.
https://digitalcommons.njit.edu/fac_pubs/3554