MRI: Acquisition of High Performance Scientific Computing Cluster at Trinity University

该仪器支持Trinity University的大规模高性能计算（HPC），从而实现了涵盖数学、计算机科学、化学、生物学和物理学的广泛科学研究工作。 Trinity正在进行的许多项目涉及“大数据”分析的问题，包括不同的领域，如主客体化学中的弱相互作用，蛋白质相互作用的多尺度模拟，与细胞膜运动相关的流体动力学模型，高通量基因组测序数据的分析，安全系统的多代理建模，以及土星环内粒子碰撞和重力的模拟。这些研究领域中的每一个都需要相当大的计算能力来分析非常大的数据集，无论是通过传统的实验室实验还是通过强大的计算模拟生成的。HPC集群为Trinity的所有研究人员提供共享计算资源，包括传统的中央处理器（CPU）和针对大量浮点数字运算进行优化的新型通用图形处理器（GPU）系统。 该集群在Linux操作系统上运行，具有混合排队/优先级系统，以便快速有效地执行任何研究人员提交的计算作业。 这种资源使Trinity University，一个本科院校，更有效地培训和教育学生的大规模计算项目和研究工作的有形方面，如测试驱动的开发，高效的算法设计，内存管理和数据可视化的价值，同时仍然在现代规模上解决开放的研究问题的挑战。

This instrument supports large-scale, high-performance computing (HPC) at Trinity University, which enables a broad range of scientific research efforts spanning mathematics, computer science, chemistry, biology, and physics. Many ongoing projects at Trinity relate to questions of 'big data' analysis, including such diverse areas as weak interactions in host-guest chemistry, multi-scale simulations of protein interactions, models of fluid dynamics related to cellular motility in membranes, analysis of high-throughput genomic sequencing data, multi-agent modeling of security systems, and simulations of particle collisions and gravity within the rings of Saturn. Each of these research areas requires considerable computational power to analyze extremely large datasets, whether generated through traditional laboratory experiments or through robust computational simulations. The HPC cluster provides a shared computational resource to all researchers at Trinity, and includes both traditional central processing units (CPUs) and newer general-purpose graphics processor unit (GPU) systems optimized for large quantities of floating-point numeric operations. The cluster operates on the Linux operating system, with a hybrid queuing/priority system so that computational jobs submitted by any researcher are quickly and efficiently executed. This resource enables Trinity University, an undergraduate institution, to more effectively train and educate students on tangible aspects of large-scale computational projects and research endeavors, such as the value of test-driven development, efficient algorithm design, memory management, and data visualization, while still tackling the challenges of open research questions on a modern scale.