Collaborative Research: Scalable Multiscale Models for the Cerebrovasculature: Algorithms, Software and Petaflop Simulations

合作研究：可扩展的脑血管多尺度模型：算法、软件和千万亿次模拟

• 批准号: 0904288
• 负责人: George Karniadakis
• 金额: $ 67.82万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0904288&HistoricalAwards=false
• 关键词: Collaborative; Research; Scalable; Multiscale; Models

Future petaflop simulations of realistic biological and physical systems will necessarily involve concurrent multiscale modeling. This project will address fundamental mathematical, algorithmic and software issues for simulating a human brain vascular model, the first of its kind, consisting of 100 large 3D arteries (Macrovascular Network, MaN), 10 million arterioles (Mesovascular Network,MeN) and one billion capillaries (Microvascular Network, MiN). The three-level MaN-MeN-MiN integration offers a general platform for developing hybrid deterministic-stochastic systems, scalable algorithms, and scalable multiscale software to handle coupling between heterogeneous PDEs and also between continuum and atomistic formulations. Building upon their initial work on the human arterial tree and the new brain imaging data, PIs propose image-based 3D Navier-Stokes simulations for fully resolving MaN, coupled to subpixel stochastic simulations of MeN and MiN to complete the closure. Project will implement an MPI/UPC hybrid model to exploit the strengths of both programming paradigms: the high scalability and rich functionality for process control in MPI, and the low communication overhead for small messages and fine-grain parallelism in UPC. We will further seek to integrate multi-threading into the MPI/UPC model, especially for dynamic refinement. The main software advancement will be the development of MPIg tailored for multiscale applications, like the MaN-MeN-MiN problem, on a single or multiple petaflop platforms. Several open issues associated with co-processing and visualization of petabyte-size data will be also addressed.Broader Impact: This work will contribute to Computational Mathematics (interfacing heterogeneous PDEs, and also PDEs-atomistic systems); to Computer Science (development of UPC/MPI, multiscale MPIg, and increased leverage of vendor-supplied MPI in MPIg); and Bioengineering (biomechanics gateway to simulate brain pathologies). This proposal is transformative in that it shifts the computational paradigm to a new level (orders of magnitude above the state-of-the-art) that will allow, for first time, realistic simulations of cerebrovasculature in health and disease. The validated algorithms for peta°op computing we propose are of general interest for use in many multiscale biological and physical applications, including vascular trees of all living organisms and also in simulations of nuclear reactors and other power/chemical plants. The new simulation environment, with the human brain as a backdrop, will be critical in training a new generation of inter-disciplinary scientists to be comfortable in using multiscale mathematics and scalable software tools for extreme computing. Project will engage postdocs, graduate, undergraduate and high school students. We will use 3D immersive/interactive visualizations as an opportunity to educate students about simulation, predictability, and other issues of computer science, engineering, and applied mathematics. Outreach activities will involve female students from middle and high schools and students from the special MET high schools.

未来对现实生物和物理系统的千万亿次模拟必然涉及并发多尺度建模。该项目将解决模拟人脑血管模型的基本数学、算法和软件问题，这是第一个此类模型，由 100 条大型 3D 动脉（大血管网络，MaN）、1000 万条小动脉（中血管网络，MeN）和 10 亿条毛细血管（微血管网络，MiN）组成。三级 MaN-MeN-MiN 集成提供了一个通用平台，用于开发混合确定性随机系统、可扩展算法和可扩展多尺度软件，以处理异构偏微分方程之间以及连续统和原子公式之间的耦合。基于他们对人体动脉树和新的大脑成像数据的初步工作，PI 提出了基于图像的 3D Navier-Stokes 模拟来完全解析 MaN，并结合 MeN 和 MiN 的亚像素随机模拟来完成闭合。 项目将实现 MPI/UPC 混合模型，以利用两种编程范例的优势：MPI 中过程控制的高可扩展性和丰富功能，以及 UPC 中小消息和细粒度并行性的低通信开销。我们将进一步寻求将多线程集成到 MPI/UPC 模型中，特别是动态细化。主要的软件进步将是为多尺度应用量身定制的 MPIg 开发，例如单个或多个 petaflop 平台上的 MaN-MeN-MiN 问题。与 PB 级数据的协同处理和可视化相关的几个开放问题也将得到解决。 更广泛的影响：这项工作将有助于计算数学（异构偏微分方程以及偏微分方程原子系统的接口）；计算机科学（UPC/MPI、多尺度 MPIg 的开发以及在 MPIg 中增加供应商提供的 MPI 的利用）；和生物工程（模拟大脑病理学的生物力学门户）。该提案具有变革性，因为它将计算范式提升到了一个新的水平（比最先进的水平高出几个数量级），这将首次允许对健康和疾病中的脑血管系统进行真实的模拟。我们提出的经验证的 peta°op 计算算法在许多多尺度生物和物理应用中受到普遍关注，包括所有生物体的维管树以及核反应堆和其他发电厂/化工厂的模拟。以人脑为背景的新模拟环境对于培训新一代跨学科科学家熟练使用多尺度数学和可扩展软件工具进行极限计算至关重要。该项目将吸引博士后、研究生、本科生和高中生。我们将使用 3D 沉浸式/交互式可视化作为一个机会来教育学生有关计算机科学、工程和应用数学的模拟、可预测性和其他问题。外展活动将涉及初中和高中的女学生以及 MET 特殊高中的学生。