II-New: Infrastructure for Supporting Biomedical Application Algorithms, Runtime Development and Resource Management

由资助的计算基础设施支持的研究重点是生物医学应用，这些应用必须（i）处理巨大尺寸的图像或图形数据，（ii）处理具有噪声和变形的数据，以及（iii）需要巨大的计算能力。正在研究的一个示例问题是使用大型3D显微镜荧光图像研究乳腺癌脑转移。这样的生物医学应用可以极大地受益于适当的异构计算基础设施和从算法开发到算法到计算基础设施的有效映射的协同努力。此外，基础设施将提供一个平台，用于实施足够严格的CS培训计划，供教师，博士后研究人员和学生与同行机构分享，并将整合到多个现有的外展计划中，以代表性不足的少数民族，高中教师和学生，如圣母大学夏季学者研究计算轨道和NSF REU计算科学网站计划。一个自上而下，垂直整合的主题是通过在拟议的研究工作。这项工作的成功完成将推动计算方法的发展，以解决治疗癌症和其他疾病的基本问题，并发现模仿生物系统的感官处理和推理能力的新算法。这项工作还将提供强大的可视化和图形分析技术，用于处理和从大型复杂的图像/图形数据中进行推断，这对许多生物医学和其他应用至关重要。最后，它将导致应用程序驱动的资源管理机制的发展，这是不可或缺的有效利用任何基于GPU的异构计算基础设施。

The research supported by the funded computing infrastructure focuses on biomedical applications that must (i) process images or graph data of enormous size, (ii) handle data with noise and deformation, and (iii) require tremendous computational capability. One example problem being investigated is studying breast cancer brain metastasis using large 3D microscopy fluorescence images. Such biomedical applications can benefit greatly from both appropriate heterogeneous computing infrastructure and concerted effort spanning from algorithm development to effective mapping of algorithms to the computing infrastructure. Furthermore, the infrastructure will provide a platform for implementing a CS training plan of sufficient rigor for faculty, postdoctoral researchers and students to share with peer institutions, and will be integrated into multiple existing outreach programs to underrepresented minority, high school teachers and students such as the Notre Dame Summer Scholars Research Computing Track and the NSF REU Site program in Computational Science. A top-down, vertically integrated theme is adopted in the proposed research effort. Successful completion of the work will advance computational approaches for tackling fundamental problems in treating cancer and other diseases, and for discovering new classes of algorithms that mimic the sensory processing and reasoning abilities of biological systems. The effort will also offer powerful visualization and graph analysis techniques for processing and making inferences from large and complex image/graph data, which are essential for many biomedical and other applications. Finally, it will lead to the development of application-driven resource management mechanisms that are indispensable for effectively exploiting any GPU-based heterogeneous computing infrastructure.