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High Performance Adaptive Physics Refinement to Enable Large-Scale Tracking of Cancer Cell Trajectory.

Publication ,  Conference
Puleri, DF; Roychowdhury, S; Balogh, P; Gounley, J; Draeger, EW; Ames, J; Adebiyi, A; Chidyagwai, S; Hernández, B; Lee, S; Moore, SV ...
Published in: Proceedings. IEEE International Conference on Cluster Computing
September 2022

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.

Duke Scholars

Published In

Proceedings. IEEE International Conference on Cluster Computing

DOI

EISSN

2168-9253

ISSN

1552-5244

Publication Date

September 2022

Volume

2022

Start / End Page

230 / 242
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Puleri, D. F., Roychowdhury, S., Balogh, P., Gounley, J., Draeger, E. W., Ames, J., … Randles, A. (2022). High Performance Adaptive Physics Refinement to Enable Large-Scale Tracking of Cancer Cell Trajectory. In Proceedings. IEEE International Conference on Cluster Computing (Vol. 2022, pp. 230–242). https://doi.org/10.1109/cluster51413.2022.00036
Puleri, Daniel F., Sayan Roychowdhury, Peter Balogh, John Gounley, Erik W. Draeger, Jeff Ames, Adebayo Adebiyi, et al. “High Performance Adaptive Physics Refinement to Enable Large-Scale Tracking of Cancer Cell Trajectory.” In Proceedings. IEEE International Conference on Cluster Computing, 2022:230–42, 2022. https://doi.org/10.1109/cluster51413.2022.00036.
Puleri DF, Roychowdhury S, Balogh P, Gounley J, Draeger EW, Ames J, et al. High Performance Adaptive Physics Refinement to Enable Large-Scale Tracking of Cancer Cell Trajectory. In: Proceedings IEEE International Conference on Cluster Computing. 2022. p. 230–42.
Puleri, Daniel F., et al. “High Performance Adaptive Physics Refinement to Enable Large-Scale Tracking of Cancer Cell Trajectory.Proceedings. IEEE International Conference on Cluster Computing, vol. 2022, 2022, pp. 230–42. Epmc, doi:10.1109/cluster51413.2022.00036.
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. 2022. p. 230–242.

Published In

Proceedings. IEEE International Conference on Cluster Computing

DOI

EISSN

2168-9253

ISSN

1552-5244

Publication Date

September 2022

Volume

2022

Start / End Page

230 / 242