CC* Compute: High-Performance Computing Backbone for Accelerating Campus-Wide and Regional Research

维拉诺瓦大学（Villanova University）正在收购一种高性能计算资源，以扩展至少27名工程、物理科学和社会科学领域的研究人员的能力。该资助还通过参与计算研究，通过创建和修改十门本科和研究生课程进行教育，以及对学生进行高性能计算操作培训，为学生准备STEM劳动力。这笔拨款的影响远远超出了维拉诺瓦大学，它建立了宾夕法尼亚东南部高性能计算联盟，在维拉诺瓦大学和非维拉诺瓦大学的研究人员之间创造了新的合作机会，并将维拉诺瓦大学连接到更广泛的开放科学网格网络，向全国的研究人员分配资源。这笔拨款集中在三个目标上，以扩大维拉诺瓦大学的研究和教育计算基础设施。首先，这项工作建立了新的计算硬件——包括1184个中央处理器（cpu）、10240个图形处理单元（gpu）和448 TB的数据存储——以及补充的软件和网络资源。其次，资源利用扩大了与美国国家科学基金会使命相关的七个项目领域的基础研究，包括(1)聚变能应用材料，(2)各种鼻窦疾病的原因，(3)储能装置中的离子传输，(4)语音感知和语言处理，(5)河流行为，(6)非线性力学行为，(7)机器学习算法。最后，开发了一些实践来降低与增长和维护计算资源相关的成本。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Villanova University is acquiring a high-performance computing resource to expand the capabilities of at least 27 identified researchers in engineering, physical sciences, and social sciences. The grant also prepares students for the STEM workforce through engagement in computational research, education via the creation and modification of ten undergraduate and graduate courses, and student training in high performance computing operations. The impact of this grant extends well beyond Villanova University by establishing the Southeastern Pennsylvania High-Performance Computing Consortium to create new collaborative opportunities between Villanova and non-Villanova researchers, and by connecting Villanova to the broader Open Science Grid network to distribute resources to researchers nationally.The grant focuses on three objectives to expand Villanova’s computational infrastructure for research and education. First, this work establishes new computational hardware – including 1,184 central processing units (CPUs), 10,240 graphical processing units (GPUs), and 448 terabytes (TB) of data storage – along with complementary software and networking resources. Second, resource usage expands fundamental research in seven project areas relevant to the mission of the National Science Foundation, including (1) materials for fusion energy applications, (2) causes of various nasal sinus diseases, (3) ion transport in energy storage devices, (4) speech perception and language processing, (5) river behavior, (6) nonlinear mechanical behavior, and (7) machine learning algorithms. Finally, practices are developed to mitigate the costs associated with growing and maintaining the computing resource.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.