Equipment: MRI: Track 1 Acquisition of Current Hardware to Enhance Computational Research on the ELSA High Performance Computing Cluster at The College of New Jersey

在这个项目中，新泽西学院（TCNJ）将获得设备，以显着升级和增强我们的科学与分析电子实验室（艾尔莎）高性能计算（HPC）集群。作为一个主要的本科院校，TCNJ是全国公认的本科生参与研究。 科学教师的学校密切合作，在他们的实验室在整个学年，并在夏季在指导本科生暑期体验（MUSE），从tCNJ获得至少一个学期的研究经验的科学毕业生的75%以上。在这个项目的过程中，每年近100名本科生研究人员将受益于从事教师指导的研究实验室从所有科学系（物理，化学，生物学，数学和统计学，计算机科学）以及土木工程学院。这些研究经验对学生来说是变革性的，使他们能够接受计算培训，并促进他们在机器学习，天体物理学，生物物理学，数学生物学和生物信息学等领域的研究。除了研究实验室，科学教师将艾尔莎纳入他们的教学，每年有800-1000名本科生接触先进的计算。TCNJ还致力于为所有STEM学生创造一个研究密集型的环境，通过增加代表性不足的学生和那些有高经济需求的学生的成功，包括那些从当地社区学院转入TCNJ的学生。通过与开放科学网格的合作访问艾尔莎正在刺激研究计划并促进集体影响的联盟。TCNJ艾尔莎集群是位于TCNJ HPC中心的异构HPC集群。这是一个国家的最先进的资源，将继续满足TCNJ的科学教师和本科生的当前和未来的计算需求。通过该奖项提供的增强功能将包括收购高端GPU，具有快速互连和大内存容量的快速现代CPU以及基于网络的高速存储。升级将直接有利于18 TCNJ教师的研究计划，使他们能够继续从事本科生在变革性的研究经验。设计一个GPU节点和CPU节点连接到高速存储的系统的目标是使艾尔莎能够运行一套不同的研究工作流程，反映TCNJ教师进行的各种跨学科计算研究。这些教师的工作跨越了一系列跨学科的主题，包括（1）计算物理学，（2）数学/计算生物学，（3）基因组学，（4）机器学习和其他领域。该集群将支持的各种科学努力的一些例子包括细菌菌毛生物力学的分子模拟研究，评估环双星行星的可居住性，使用数学模型探索海洋无脊椎动物游泳性能的进化权衡，采用基因组方法表征植物防御害虫的新型调节剂，了解如何降低机器学习中的训练数据注释成本。最终，该项目中对艾尔莎集群的增强将显著提高科学发现的能力，并帮助TCNJ教师为本科生做好准备，以便在未来的职业生涯中利用日益强大的HPC资源。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

In this project, The College of New Jersey (TCNJ) will acquire equipment to significantly upgrade and enhance our Electronic Laboratory for Science and Analysis (ELSA) High Performance Computing (HPC) cluster. As a primarily undergraduate institution, TCNJ is nationally recognized for the engagement of undergraduate students in research. School of Science faculty work closely with undergraduates in their laboratories throughout the academic year and during the summer in the Mentored Undergraduate Summer Experience (MUSE), with more than 75% of science graduates from TCNJ obtaining at least a semester of research experience. Over the course of this project, nearly 100 undergraduate student researchers per year will benefit by engaging in faculty-mentored research in labs from all School of Science departments (Physics, Chemistry, Biology, Mathematics and Statistics, and Computer Science) as well as in Civil Engineering. These research experiences are transformative for the students enabling their training in computing as well as facilitating their research in fields that include machine learning, astrophysics, biophysics, mathematical biology, and bioinformatics. Beyond the research laboratory, science faculty incorporate ELSA in their teaching, exposing 800-1000 undergraduates per year to advanced computing. TCNJ is also committed to creating a research-intensive environment for all STEM students by increasing success among underrepresented students and those with high financial need, including those that are transferring to TCNJ from local community colleges. The access to ELSA through collaboration with Open Science Grid is stimulating research programs and fostering alliances for collective impact.The TCNJ ELSA cluster is a heterogeneous HPC cluster housed in the TCNJ HPC Center. It is a state-of-the-art resource that will continue to meet the current and future computational needs of TCNJ’s science faculty and undergraduate students. The enhancements provided through this award will include the acquisition of high-end GPUs, fast, modern CPUs with fast interconnects and large memory capacities, and high speed network-based storage. The upgrades will directly benefit the research programs of 18 TCNJ faculty members, allowing them to continue to engage undergraduate students in transformative research experiences. The objective in designing a system with both GPU nodes and CPU nodes connected to high speed storage is to enable ELSA to run a diverse set of research workflows that reflect the varied and interdisciplinary computational research carried out by TCNJ faculty. The work of these faculty spans a range of interdisciplinary themes including (1) computational physics, (2) mathematical/computational biology, (3) genomics, (4) machine learning, and other areas. Some of the examples of the diverse scientific efforts that the cluster will support include molecular simulation studies of bacterial pilus biomechanics, assessing the habitability of circumbinary planets, using mathematical models to explore evolutionary tradeoffs in swimming performance across marine invertebrates, employing genomic approaches to characterize novel regulators of plant defenses against pests, and understanding how to reduce training data annotation costs in machine learning. Ultimately, the enhancements to the ELSA cluster in this project will significantly improve capacity for scientific discovery, and help TCNJ faculty prepare undergraduate students to leverage the increasingly powerful HPC resources of the future in their careers.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.