The human body contains approximately 20 billion blood vessels, which transport nutrients, oxygen, immune cells, and signals throughout the body. The brain's vasculature includes up to 9 billion of these vessels to support cognition, motor processes, and myriad other vital functions. To model blood flowing through a vasculature, a geometric description of the vessels is required. Previously reported attempts to model vascular geometries have produced highly-detailed models. These models, however, are limited to a small fraction of the human brain, and little was known about the feasibility of computationally modeling whole-organ-sized networks. We implemented a fractal-based algorithm to construct a vasculature the size of the human brain and evaluated the algorithm's speed and memory requirements. Using high-performance computing systems, the algorithm constructed a vasculature comprising 17 billion vessels in 1960 core-hours, or 49 minutes of wall-clock time, and required less than 32 GB of memory per node. We demonstrated strong scalability that was limited mainly by input/output operations. The results of this study demonstrated, for the first time, that it is feasible to computationally model the vasculature of the whole human brain. These findings provide key insights into the computational aspects of modeling whole-organ vasculature.

中文翻译：

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模拟全器官血管网络的计算可行性

人体包含大约 200 亿条血管，它们将营养物质、氧气、免疫细胞和信号输送到全身。大脑的脉管系统包括多达 90 亿条血管，用于支持认知、运动过程和无数其他重要功能。为了模拟流经脉管系统的血液，需要对血管进行几何描述。先前报道的对血管几何形状进行建模的尝试已经产生了高度详细的模型。然而，这些模型仅限于人类大脑的一小部分，并且对于对整个器官大小的网络进行计算建模的可行性知之甚少。我们实施了一种基于分形的算法来构建人脑大小的脉管系统，并评估了算法的速度和内存要求。使用高性能计算系统，该算法在 1960 个核心小时或 49 分钟的挂钟时间内构建了一个由 170 亿个血管组成的脉管系统，并且每个节点需要不到 32 GB 的内存。我们展示了主要受输入/输出操作限制的强大可扩展性。这项研究的结果首次证明，对整个人类大脑的脉管系统进行计算建模是可行的。这些发现为建模全器官脉管系统的计算方面提供了关键见解。对整个人脑的脉管系统进行计算建模是可行的。这些发现为建模全器官脉管系统的计算方面提供了关键见解。对整个人脑的脉管系统进行计算建模是可行的。这些发现为建模全器官脉管系统的计算方面提供了关键见解。