Papers Published

1. 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 [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.