MRI-R2 : Acquisition of a Heterogenous Terascale Shared Campus Computing Facility

“该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。“提案编号：09- 59382 PI（s）：Fisher，Robert T.; Cowles，Geoffrey; W.，作者：Gottlieb; Sigal，卡纳，Gaurav; Wang，Cheng North达特茅斯，MA 02727- 2300标题： MRI-R2：收购一个异构的Terascale共享校园计算FacilityProject Proposed：这个项目，收购一个异构的Terascale并行计算机集群，包括图形处理单元（GPU），服务于一个跨学科和多学科的数学家和计算科学家和工程师，以及他们的本科生和研究生。 这个共享的校园研究仪器利用了麻省大学达特茅斯分校现有的没有GPU的小规模集群，从而形成了一个超过四倍大小的组合系统。 具体来说，拟议的集群包含32个8路节点（256个核心），每个节点加上一个NVIDIA Tesla GPU，总共32个GPU。 该集群并不打算成为一个通用的计算资源，而是一个高度专业化的工具，这是提案中研究人员小组为科学计算开发高性能算法所必需的。 研究人员将利用每个节点中GPU的合并来开发混合并行和基于众核架构的计算。研究人员将把这些GPU加速的求解器抽象成一个开源的GPU科学库（GPUSL），它将通过一组定义良好的接口与上面的模拟代码完全分离。 来自麻省大学达特茅斯分校物理、数学、海洋科学和土木工程系的教师组成了一个科学计算小组，该小组将成为共享计算资源的核心管理和用户组。 该小组的研究重点范围从巨型分子云的演变（使用FLASH代码）到沿海海洋建模（使用FVCOM代码）到不可压缩湍流对流流体的模拟。 该小组的活动包括研究合作，每周团队会议，以分享数值算法的专业知识，学生的共同建议，以及多学科博士课程的发展。该集群将位于马萨诸塞大学达特茅斯分校的校园信息技术服务（CITS）数据中心，并由CITS企业系统管理团队直接支持。 该团队将负责用户磁盘空间的预防性维护会话备份和恢复。 计算机的使用权及其分配将由一个研究用户组委员会处理，该委员会将负责向寻求计算机时间的教职员工征求建议，并负责平衡用户群中教职员工、研究生和本科生的研究需求。该集群的可用性允许下一代高性能计算专家的培训，并支持新的跨学科博士学位。计算机科学，目前正在开发中。 研究生受益于在多学科环境中为各种具有挑战性的物理问题开发尖端并行和GPU加速算法的机会。 该集群还直接有利于本科教育，作为NSF资助的本科数学学生计算科学（CSUMS）项目的一部分，该项目旨在为本科生做好准备，并让他们参与计算数学的持续研究经验。 该方法是招募有前途的高中毕业生，然后通过计算科学的研究课题指导大一和二年级学生，并通过精心挑选的研究项目指导高年级学生达到高潮。此外，麻省大学达特茅斯分校有一个成功的传统，即通过其“现在大学迈向抽象推理和思维”（START）计划，为代表性不足的学生扩大高等教育的机会。在其40年的历史中，该计划在扩大STEM向弱势学生提供妇女和代表性不足的少数民族方面发挥了重要作用;特别是，近几个学期约有25% - 30%的START学生是代表性不足的少数族裔。对于科学界来说，在这个支持集群上开发的所有计算机代码，包括GPU软件库，都将通过一个专门的网站发布，立即影响全球2500多名使用社区代码FLASH和FVCOM的研究人员。

"This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5)." Proposal #: 09-59382PI(s): Fisher, Robert T.; Cowles, Geoffrey; W., Gottlieb; Sigal, Khanna, Gaurav; Wang, ChengInstitution: University of Massachusetts-Dartmouth North Dartmouth, MA 02727-2300Title: MRI-R2: Acquisition of a Heterogeneous Terascale Shared Campus Computing FacilityProject Proposed :This project, acquiring a heterogeneous terascale parallel computer cluster incorporating graphics processing units (GPUs), services an inter- and multi-disciplinary group of mathematicians and computational scientists and engineers, and their undergraduate and graduate students. This shared campus research instrument leverages an existing small-scale cluster without GPUs at UMass-Dartmouth resulting in a combined system of more than four times the size. Specifically, the proposed cluster contains 32 8-way nodes (256 cores) plus one NVIDIA Tesla GPU per node for a total of 32 GPUs. The cluster is not intended to be a general computing resource, but rather highly specialized instrument necessary for the development of high-performance algorithms for scientific computation by the group of researchers in the proposal. Researchers will take advantage of the incorporation of the GPUs in each node to develop hybrid parallel and many-core-architecture-based computing. The investigators will abstract out these GPU-accelerated solvers into an open source GPU Scientific Library (GPUSL), which will be cleanly separated from the overlying simulation codes through a set of well-defined interfaces. Faculty members from the Physics, Mathematics, Marine Science and Civil Engineering departments at UMass-Dartmouth have formed a scientific computing group that will become the core management and user group of the shared computational resources. The research foci of the group range from Evolution of Giant Molecular Clouds (using the FLASH code) to Coastal Ocean Modeling (using the FVCOM code) to the Simulation of an Incompressible Turbulent Convective Fluid. Activities of this group include research collaborations, weekly team meetings for the sharing of expertise on numerical algorithms, co-advising of students, and a development of a multi-disciplinary doctoral program. The cluster will be housed in the Campus Information Technology Services (CITS) data center at UMass-Dartmouth, and directly supported by the CITS Enterprise Systems Administration Team. This team will be responsible for preventive maintenance session backup and recovery of user disk space. The access to the machine and its allocation will be handled by a Research User Group Committee which will be responsible for soliciting proposals from faculty members seeking computer time and will also be responsible for balancing the research needs of the faculty, graduate, and undergraduate students in the user base.Broader Impacts: The availability of this cluster allows for the training of the next generation of high performance computing experts, and supports a new interdisciplinary Ph.D. program in computational science, currently in development. Graduate students benefit from the opportunity to develop cutting-edge parallel and GPU-accelerated algorithms for a variety of challenging physical problems, in a multi-disciplinary setting. The cluster also directly benefits undergraduate education, as part of the NSF-funded Computational Science for Undergraduate Mathematics Students (CSUMS) project, which aims to prepare undergraduate students for, and engage them in, a sustained research experience in computational mathematics. The approach is to recruit promising high school seniors, then guide freshman and sophomores students through research topics in computational science, and culminate by mentoring seniors through carefully selected research projects.In addition, UMass-Dartmouth has a successful tradition of broadening higher-education access to underrepresented students through its College Now Steps Toward Abstract Reasoning and Thinking (START) program. Over its 40-year history, the program has played a significant role in broadening STEM access to disadvantaged students to women and underrepresented minorities; in particular, approximately 25% - 30% of START students in recent semesters are underrepresented minority persons of color. For the scientific community, all computer codes developed on this enabling cluster, including the GPU Software Library, will be released via a dedicated website, immediately impacting more than 2500 researchers worldwide who use the community codes FLASH and FVCOM.