"High Performance Adaptive Physics Refinement to Enable Large-Scale Tra" by Daniel F. Puleri, Sayan Roychowdhury et al.
 

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

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