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MRI: Acquisition of a High-Performance Computing Cluster to Unveil the Sources of Gravitational Waves

MRI：购买高性能计算集群来揭示引力波的来源

基本信息

批准号：
1726951
负责人：
Vassiliki Kalogera
金额：
$ 34.99万
依托单位：
Northwestern University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-15 至 2019-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1726951&HistoricalAwards=false
关键词：
MRI Acquisition Performance Computing Cluster

项目摘要

This grant will allow the purchase of a high-performance-computing cluster, which is required by researchers at Northwestern University to investigate sources of gravitational-wave emission. Modeling and understanding of gravitational-wave sources is becoming extremely important as scientists are utilizing data from the Laser Interferometer Gravitational-wave Observatory to detect black holes and other exotic astrophysical objects (such as "neutron stars"). On one level, astronomers require large computers to develop programs that can help us understand the large amounts of data from gravitational-wave detectors, and pick out the real, astronomical sources from a wide variety of noise sources. But in addition, as detections of gravitational-wave sources occur more frequently, astronomers need to understand what those detections can tell us about the population of exotic objects throughout the universe. For instance, given the detections of gravitational-wave sources already, researchers ask how many black holes there might be, in our galaxy, and in other galaxies, and how massive are they? Also, researchers ask: how do those black holes form, and how do systems of pairs of black holes form? These are important questions in understanding objects at the extreme of the known laws of physics, and to answer these questions, astronomers need to understand not only the lives of individual stars and the formation of black holes and neutron stars, but they need to simulate populations of millions of stars, and the wide variety of events and processes that can affect the development of those stars. For that work, astrophysicists require large collections of very efficient computers, such as the computer cluster that will be purchased with these funds, to run massive simulations that help us explore these questions. In addition to the pure research that will be done with this cluster, the PI's group is also well known for generating award-winning visualizations of their simulations, which will be used at a variety of Science, Technology, Engineering, and Mathematics (STEM) education and public-outreach events. The entire team also has extensive experience in attracting a diverse group of researchers to STEM research and fields; this cluster will help that group to train a new generation of diverse researchers in data-science methods and scientific computing, contributing to the technical workforce of the nation. This grant will allow the acquisition of a high-performance computing (HPC) cluster that will enable research in the emerging area of gravitational physics. The cluster will be essential to the development and optimization of codes needed for both gravitational-wave (GW) data analysis as part of the Laser-Interferometer Gravitational-wave Observatory (LIGO) Scientific Collaboration, and GW source modeling for the physical interpretation of the detections. The equipment consists of a large computer cluster (56 nodes) with InfiniBand networking and 70 TB of usable storage. This cluster will incorporate three innovative Graphics-Programming-Unit nodes (using GPUs appropriate for scientific computing) which will be used as accelerators for special-purpose massively parallel computations. The cluster will be housed at a top-of-the-line HPC data center on the Northwestern University campus and will be operated and managed by an experienced team of HPC professionals led by one of the co-PIs. In more detail, the cluster will be used for GW research focused on binaries with two compact objects (neutron stars and/or black holes) in interdisciplinary collaborations between GW data analysts (members of the LIGO Scientific Collaboration), astrophysicists, and computer scientists. The goals are to optimize data searches for GW signals (through effective detector characterization), to extract as promptly as possible and accurately the physical properties of the signal sources (through continuous improvements of our parameter-estimation algorithms), and to advance the astrophysical interpretation of the discoveries so we can better understand the sources' origin and constrain theoretical models using our GW observations (through the development of state-of-the-art formation models in different environments and comparing predictions to data). The team is led by PI Kalogera and co-PI Rasio, who are well recognized for their significant impact in these research areas and for their innovative development of new computational tools for GW data analysis and astrophysical modeling of GW sources.

这笔赠款将允许购买一个高性能计算集群，这是西北大学研究人员调查引力波发射源所需的。由于科学家们正在利用激光干涉仪引力波观测站的数据来探测黑洞和其他奇异天体（如“中子星”），对引力波源的建模和理解变得极为重要。在某种程度上，天文学家需要大型计算机来开发程序，帮助我们理解来自引力波探测器的大量数据，并从各种各样的噪声源中挑选出真实的天文源。但除此之外，随着引力波源的探测越来越频繁，天文学家需要了解这些探测可以告诉我们宇宙中奇异物体的数量。例如，考虑到已经探测到引力波源，研究人员会问，在我们的星系和其他星系中可能有多少黑洞，它们的质量有多大？此外，研究人员还问：这些黑洞是如何形成的，以及黑洞对系统是如何形成的？这些都是在已知物理定律的极端情况下理解物体的重要问题，为了回答这些问题，天文学家不仅需要了解单个恒星的生命以及黑洞和中子星的形成，还需要模拟数百万颗恒星的种群，以及可能影响这些恒星发展的各种事件和过程。为了完成这项工作，天体物理学家需要大量非常高效的计算机，比如将用这些资金购买的计算机集群，来运行大规模的模拟，帮助我们探索这些问题。除了将使用该集群进行的纯研究外，PI的团队还以生成获奖的模拟可视化而闻名，这些可视化将用于各种科学，技术，工程和数学（STEM）教育和公共宣传活动。整个团队在吸引不同的研究人员到STEM研究和领域方面也有丰富的经验;这个集群将帮助该小组在数据科学方法和科学计算方面培养新一代不同的研究人员，为国家的技术劳动力做出贡献。这笔赠款将允许收购一个高性能计算（HPC）集群，这将使引力物理学的新兴领域的研究成为可能。该集群对于开发和优化作为激光干涉仪引力波天文台（LIGO）科学合作的一部分的引力波（GW）数据分析所需的代码以及用于探测物理解释的GW源建模至关重要。该设备包括一个大型计算机集群（56个节点），带有InfiniBand网络和70 TB的可用存储。该集群将包含三个创新的图形编程单元节点（使用适用于科学计算的GPU），这些节点将用作专用大规模并行计算的加速器。该集群将位于西北大学校园内的顶级HPC数据中心，并将由一个经验丰富的HPC专业人员团队运营和管理，该团队由一名合作PI领导。更详细地说，该集群将用于GW研究，重点是GW数据分析师（LIGO科学合作组织的成员），天体物理学家和计算机科学家之间的跨学科合作中的两个紧凑物体（中子星和/或黑洞）。目标是优化GW信号的数据搜索（通过有效的检测器表征），尽可能迅速和准确地提取信号源的物理特性（通过不断改进我们的参数估计算法），并推进对这些发现的天体物理学解释，以便我们能够更好地理解源的起源，并利用我们的GW观测约束理论模型（通过在不同环境中开发最先进的地层模型并将预测与数据进行比较）。该团队由PI Kalogera和co-PI Rasio领导，他们在这些研究领域的重大影响以及创新开发用于GW数据分析和GW源天体物理建模的新计算工具而备受认可。