MRI: Acquisition of a Flexible High-Performance Computing System for Data and Compute Driven Scientific Discovery

该项目为数据和计算驱动的科学发现获取高性能计算（HPC）资源，旨在替换和扩展该机构HPC基础设施核心的过时设备。由于拟议的配置旨在特别解决当前基础设施的缺陷，从而使其适应其不断扩大的用户群的需求，因此获得这种仪器应该可以增强当前的HPC设施，并在广泛的前沿实现转型研究。集中、共享的校园HPC设施减少了访问计算资源的障碍，特别是对于来自非传统HPC用户的学科的初级科学家、学生和教师。该设备补充了现有和未来的投资，并有助于推动科学技术学院（CST）和整个寺庙社区的相关教师和本科生研究和教育活动。该机构拥有约38，000名研究生和本科生，拥有非常多样化的学生群体。凭借其庞大的共享内存池，所请求的机器将是大费城地区的第一台此类机器，该地区拥有高科技和IT相关产业的高度集中。他们将获得1。一个大的共享内存机器与9TB的可寻址RAM，2。一个可扩展、可伸缩的并行存储设备，容量为1.5 PB，3个60节点的传统Linux集群，针对中小型并行计算进行了调整。计算科学、材料科学、生物物理学、数据分析和基因组学是贯穿CST研究最活跃领域的主要主题。各种中心和机构反映了这些主题，如1。基因组学和进化医学研究所，2，计算分子科学研究所，3。数据分析和生物医学信息学中心，4。生物物理学和计算生物学中心，和5。功能层状材料计算设计中心。CST与工程学院和文科学院、商学院和药学院、福克斯蔡斯癌症中心和医学院一起，旨在广泛影响大数据、纳米科学、成像、神经科学以及能源和健康方面的研究。

This project, acquiring High-Performance Computing (HPC) resources for data and compute-driven scientific discovery, aims to replace and extend outdated current equipment at the core of the institution's HPC infrastructure. Since the proposed configuration is designed to particularly address deficiencies of the current infrastructure and thus adapt it to the needs of its widening user base, acquiring this instrumentation should enhance current HPC facilities and enable transformational research over a broad front. Central, shared, on-campus HPC facilities reduce barriers to access computational resources, especially for junior scientists, students, and faculty from disciplines that are not traditional HPC users. This equipment complements existing and future investments and helps drive related faculty and undergraduate research and educational activities across the College of Science and Technology (CST) and the entire Temple community. With an enrollment around 38,000 graduate and undergraduate students, the institution boasts of a very diverse student body. With its large shared memory pool, the requested machine will be the first of its kind in the Greater Philadelphia region that has a high concentration of high-tech and IT-related industries. They will obtain 1. A large shared memory machine with 9TB addressable RAM, 2. An extensible, scalable parallel storage appliance with 1.5PB capacity, and 3 A 60-node conventional Linux cluster tuned for small to medium size parallel calculations. Computational science, materials science, biophysics, data analytics, and genomics are major themes running through the most active areas of CST research. Various centers and institutes reflect these themes, such as the 1. Institute for Genomics and Evolutionary Medicine, 2, Institute for Computational Molecular Science, 3.Center for Data Analytics and Biomedical Informatics, 4. Center for Biophysics and Computational Biology, and 5. Center for Computational Design of Functional Layered Materials. CST's, in tandem with the Colleges of Engineering and Liberal Arts, the Schools of Business and Pharmacy, the Fox Chase Cancer Center, and the School of Medicine aim to broadly impact research in big data, nano-scale science, imaging, neuroscience, as well as energy and health.