MRI: Acquisition of a Computing Cluster for Portland Institute for Computational Sciences

这家研究企业获取、部署和维护用于科学计算的现代异类集群。该集群将最新的多核处理器与新的MIC(多集成核心)架构相结合，为开发新的并行数学算法提供了试验台。通过一个名为波特兰计算科学研究所的新中心管理的多机构对设备的访问，将在计算科学家的区域用户基础内催化互动。该设备将大大加强俄勒冈州开放的、全州范围的高性能计算接入。该设备旨在对计算数学中的根本新技术的研究产生深远影响。利用美国国家实验室开发的现有并行化工具的高度可扩展的降维技术将被设计用于解决时空域中提出的演化问题。对于复杂时空结构上的双曲问题，将研究一类新的数学上合理的方法，该方法非常适合于MICs上的线程并行。基于算子值轮廓积分的可并行化近似，研究了计算偏微分算子本征模的新算法。另一个项目将使用大量的医学数据集来验证新的统计推断方法，从而得出有价值的疾病进展模板。该设备将被广泛用于开发和运行地幔对流的数值模拟。进一步编目的项目实例表明，该设备的影响将是地区性的，甚至超出俄勒冈州。教育和培训活动将促进对开发新的和正在出现的节能处理器至关重要的细粒度并行的学习。

This research enterprise acquires, deploys, and maintains a modern heterogeneous cluster for scientific computing. The cluster combines the latest multi-core processors with the new MIC (Many Integrated Core) architecture to provide a testbed for development of new parallel mathematical algorithms. Multi-institutional access to the equipment, managed through a new center called the Portland Institute for Computational Sciences, will catalyze interactions within a regional user base of computational scientists. This equipment will considerably strengthen open, state-wide high performance computing access in Oregon. The equipment aims to profoundly impact research into fundamentally new techniques in computational mathematics. Highly scalable dimension reduction techniques that exploit existing parallelization tools developed at US national laboratories will be designed for evolution problems posed in spacetime domains. For hyperbolic problems on complex spacetime structures, a new class of mathematically sound methods, well-suited for thread parallelism on MICs, will be studied. New algorithms to compute eigenmodes of partial differential operators, based on parallelizable approximations of operator-valued contour integrals, are considered. Another project will use massive medical data sets to validate novel statistical inferential methods leading to valuable templates of disease progression. The equipment will be extensively used to develop and run numerical simulations of convection in Earth's mantle. Further catalogued examples of projects show that the equipment's impact will be regional and beyond Oregon. Educational and training activities will promote the learning of fine-grained parallelism critical to exploit new and emerging energy-efficient processors.