COMPUTATION RESEARCH in BOSTON and BEYOND (CRiBB)

DateNov. 7, 2014
Speakers Amanda Randles (Lawrence Livermore National Laboratory)
Topic Massively Parallel Simulations of Patient-Specific Hemodynamics
Abstract:  The recognition of the role hemodynamic forces have in the localization and development of disease has motivated large-scale efforts to enable patient-specific simulations. When combined with computational approaches that can extend the models to include physiologically accurate hematocrit levels in large regions of the circulatory system, these image-based models yield insight into the underlying mechanisms driving disease progression and inform surgical planning or the design of next generation drug delivery systems. Building a detailed, realistic model of human blood flow is a formidable mathematical and computational challenge requiring large-scale fluid models as well as explicit models of suspended bodies like red blood cells. This will require high resolution modeling of cells in the blood stream, and necessitate significant computational advances. To date, we have efficiently scaled our algorithms to run on up to 294,912 processors and are working to extend this scalability to allow the study of large regions of the circulatory system. Building on HARVEY, a parallel fluid dynamics application designed to model hemodynamics in patient-specific geometries, we are working to further validate the results through rigorous comparison with in vivo and in vitro measurements. We are also working to expand the scope of projects to address not only vascular diseases, but also treatment planning and the movement of circulating tumor cells in the bloodstream. In close collaboration with researchers and physicians at the Dana-Farber Cancer Institute and Brigham and Women's Hospital, we are establishing a mathematical framework that can have direct impact on patient care. In this talk, I will discuss the fluid model and provide an overview of some of the optimization methods employed to achieve highly efficient scaling on the Blue Gene/Q supercomputer. I will discuss a few examples of applications of the code.



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