High Performance Adaptive Physics Refinement to Enable Large-Scale Tracking of Cancer Cell Trajectory.
Proc IEEE Int Conf Clust Comput
; 2022: 230-242, 2022 Sep.
Article
en En
| MEDLINE
| ID: mdl-38125675
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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Colección:
01-internacional
Banco de datos:
MEDLINE
Idioma:
En
Revista:
Proc IEEE Int Conf Clust Comput
Año:
2022
Tipo del documento:
Article
País de afiliación:
Estados Unidos