High Performance Adaptive Physics Refinement to Enable Large-Scale Tracking of Cancer Cell Trajectory.
High Performance Adaptive Physics Refinement to Enable Large-Scale Tracking of Cancer Cell Trajectory.
复制标题
高性能自适应物理细化可实现大规模追踪癌细胞轨迹。
DOI：
10.1109/cluster51413.2022.00036
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Randles,Amanda
中科院分区：
文献类型：
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作者：
Puleri,DanielF;Roychowdhury,Sayan;Balogh,Peter;Gounley,John;Draeger,ErikW;Ames,Jeff;Adebiyi,Adebayo;Chidyagwai,Simbarashe;Hernández,Benjamín;Lee,Seyong;Moore,ShirleyV;Vetter,JeffreyS;Randles,Amanda
通过循环系统跟踪模拟癌细胞的能力，对于发展对转移扩散的机械理解非常重要，通过要求以细胞级分辨率模拟大体积液体，突破了当今超级计算机的极限。为了克服这一挑战，我们引入了一种新的自适应物理精化(APR)方法，该方法跨大范围捕获蜂窝规模的交互，并利用CPU-GPU混合方法来最大化性能。通过集成多物理和多分辨率模型的算法进步，我们建立了一个精细分辨窗口，其中显式建模的单元耦合到一个粗分辨的整体流体域。在这项工作中，我们通过与完全解析的流固耦合方法进行比较，提出了APR框架的多种验证方法，并使用延迟隐藏和最大化内存带宽等技术来有效地利用异质节点体系结构。总而言之，这些计算开发和性能优化提供了一个健壮和可扩展的框架，以实现对癌细胞传输的系统级模拟。
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.