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MRI: Acquisition of High Performance Computing System for Interdisciplinary Research and Teaching

MRI：购置高性能计算系统用于跨学科研究和教学

基本信息

批准号：
2117377
负责人：
Amy Wagaman
金额：
$ 50万
依托单位：
Amherst College
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2117377&HistoricalAwards=false
关键词：
MRI Acquisition Performance Computing System

项目摘要

This proposal aims to acquire a high-performance computing (HPC) cluster to support research and training in the sciences. Claiming that high performance computation is playing and increasingly significant role in nearly every facet of modern society, the institution aims to facilitate the incorporation of high-performance computing into undergraduate work across the liberal arts following its tradition of working very closely with its liberal arts students. Claiming that high performance computation plays an increasingly significant role in nearly every facet of the modern society and that research impacts extend beyond applications in STEM fields (e.g., impacting applications such as AI (Artificial Intelligence) and ML (Machine Learning), the institution would like to train students in pertinent areas that that make use of such general-purpose clusters. Both CPU and GPU computing nodes will help new users. The institution will implement support services to help users of many backgrounds in the use of the system. They expect the system’s generality and flexibility will facilitate the process in various fields (e.g.: Biology, Chemistry, Computer Science, Neurosciences).Training students in the use of the HPC technology should prepare the next generation of researchers. The new cluster will increase the capacity for leading edge research in STEM and facilitate recruitment of promising faculty candidates who seek to integrate research, training, and teaching in a liberal arts environment. The generality of the instrumentation supports collaboration and provides opportunities to explore more. Likely, many programs will utilize the cluster, contributing to the Undergraduate Fellows Program.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.

这项提议旨在获得一个高性能计算(HPC)集群，以支持科学研究和培训。该学院声称，高性能计算在现代社会的几乎每一个方面都发挥着越来越重要的作用，它的目标是按照与文科学生密切合作的传统，将高性能计算纳入整个文科的本科工作。该机构声称，高性能计算在现代社会的几乎每一个方面都发挥着越来越重要的作用，研究影响超越了STEM领域的应用(例如，影响AI(人工智能)和ML(机器学习)等应用)，该机构希望在利用此类通用集群的相关领域培训学生。CPU和GPU计算节点都将帮助新用户。该机构将实施支助服务，帮助具有多种背景的用户使用该系统。他们预计，该系统的通用性和灵活性将促进不同领域(例如：生物、化学、计算机科学、神经科学)的进程。培训学生使用HPC技术应为下一代研究人员做好准备。新的集群将增加STEM前沿研究的能力，并促进招聘寻求在文科环境中整合研究、培训和教学的有前途的教员候选人。工具的通用性支持协作，并提供了探索更多内容的机会。很可能，许多项目将利用该集群，为本科生研究员项目做出贡献。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

The Problem Solving Benefits of Down-sampling Vary by Selection Scheme

下采样解决问题的好处因选择方案而异

DOI：
10.1145/3583133.3590713
发表时间：
2023
期刊：
GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation
影响因子：
0
作者：
Boldi, Ryan;Bao, Ashley;Briesch, Martin;Helmuth, Thomas;Sobania, Dominik;Spector, Lee;Lalejini, Alexander
通讯作者：
Lalejini, Alexander

A Static Analysis of Informed Down-Samples

知情下样本的静态分析