Computation for the Endless Frontier

无尽前沿的计算

• 批准号: 1818253
• 负责人: Daniel Stanzione
• 金额: $ 6000万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1818253&HistoricalAwards=false
• 关键词: Computation; Endless; Frontier

Computation is critical to our nation's progress in science and engineering. Whether through simulation of phenomena where experiments are costly or impossible, large scale data analysis to sift the enormous quantities of digital data scientific instruments can produce, or machine learning to find patterns and suggest hypothesis from this vast array of data, computation is the universal tool upon which nearly every field of science and engineering relies upon to hasten their advance. This project will deploy a powerful new system, called "Frontier", that builds upon a design philosophy and operations approach proven by the success of the Texas Advanced Computing Center (TACC) in delivering leading instruments for computational science. Frontier provides a system of unprecedented scale in the NSF cyberinfrastructure that will yield productive science on day one, while also preparing the research community for the shift to much more capable systems in the future. Frontier is a hybrid system of conventional Central Processing Units (CPU) and Graphics Processing Units (GPU), with performance capabilities that significantly exceeds prior leadership-class computing investments made by NSF. Importantly, the design of Frontier will support the seamless transition of current NSF leadership-class computing applications to the new system, as well as enable new large-scale data-intensive and machine learning workloads that are expected in the future. Following deployment, the project will operate the system in partnership with ten academic partners. In addition, the project will begin planning activities in collaboration with leading computational scientists and technologists from around the country, and will leverage strategic public-private partnerships to design a leadership-class computing facility with at least ten times more performance capabilities for Science and Engineering research, ensuring the economic competitiveness and prosperity for our nation at large.TACC, in partnerships with Dell EMC and Intel, will deploy Frontier, a hybrid system offering 39 PF (double precision) of Intel Xeon processors, complemented by 11 PF (single precision) of GPU cards for machine learning applications. In addition to 3x the per node memory of NSF's prior leadership-class computing system primary compute nodes, Frontier will have 2x the storage bandwidth in a storage hierarchy that includes 55PB of usable disk-based storage and 3PB of 'all flash' storage, to enable next generation data-intensive applications and support for the data science community. Frontier will be deployed in TACC's state-of-the-art datacenter which is configured to supply 30% of the system's power needs from renewable energy. Frontier will include support for science and engineering in virtually all disciplines through its software environment support for application containers, as well as through its partnership with ten academic institutions providing deep computational science expertise in support of users on the system. The project planning effort for a Phase 2 system with at least 10x performance improvement will incorporate a community-driven process that will include leading computational scientists and technologists from around the country and leverage strategic public-private partnerships. This process will ensure the design of a future NSF leadership-class computing facility that incorporates the most productive near-term technologies, and anticipates the most likely future technological capabilities for all of science and engineering requiring leadership-class computational and data-analytics capabilities. Furthermore, the project is expected to develop new expertise and techniques for leadership-class computing and data-driven applications that will benefit future users worldwide through publications, training, and consulting. The project will leverage the team's unique approach to education, outreach, and training activities to encourage, educate, and develop the next generation of leadership-class computational science researchers. The team includes leaders in campus bridging, minority-serving institute (MSI) outreach, and data technologies who will oversee efforts to use Frontier to increase the diversity of groups using leadership-class computing for traditional and data-driven applications.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.

计算对于我们国家在科学和工程方面的进步至关重要。无论是通过模拟实验是昂贵的还是不可能的现象，大规模数据分析都可以筛选大量数字数据科学仪器可以产生或机器学习以找到模式并提出从这个广泛的数据中提出的假设，计算都是通用工具，几乎每个科学领域都涉及其进步。该项目将部署一个称为“ Frontier”的强大新系统，该系统以设计理念和操作方法为基于德克萨斯州高级计算中心（TACC）在提供计算科学领先的工具方面证明。 Frontier在NSF网络基础设施中提供了一个前所未有的规模系统，该系统将在第一天产生富有成效的科学，同时还为将来的研究社区做好了准备向更有能力的系统的转变。 Frontier是一种由常规中央处理单元（CPU）和图形处理单元（GPU）组成的混合系统，其性能功能大大超过了NSF进行的先前领导级计算机投资。 重要的是，Frontier的设计将支持当前NSF领导力类计算应用程序向新系统的无缝过渡，并启用未来预期的新的大规模数据密集型和机器学习工作负载。 部署后，该项目将与十个学术合作伙伴合作运营该系统。 此外，该项目将与来自全国各地的领先的计算科学家和技术人员合作开始计划活动，并将利用战略性的公私伙伴关系来设计领导力级计算设施，具有至少十倍的科学和工程研究绩效能力，以确保我们国家的经济竞争力和繁荣，以确保我们国家的经济竞争力和繁荣的范围。 Intel Xeon处理器的PF（双重精度），并由GPU卡的11 pf（单个精度）进行补充，用于机器学习应用程序。除3倍外，NSF先前的领导级计算系统的每个节点记忆主要计算节点还将在存储层次结构中具有2倍的存储带宽，其中包括55pb的基于可用磁盘的存储和3pb的“所有闪光灯”存储，以启用下一代数据密集型应用程序和对数据科学社区的支持。 Frontier将部署在TACC最先进的数据中心中，该数据中心配置为从可再生能源中提供30％的系统功率需求。 Frontier将通过其对应用程序容器的软件环境支持以及与十家学术机构的合作，从而在所有学科中包括对科学和工程的支持，从而提供了深厚的计算科学专业知识，以支持系统上的用户。具有至少10倍性能改进的2阶段系统的项目计划工作将结合一个社区驱动的流程，该过程将包括来自全国各地的计算科学家和技术人员的领先，并利用战略性的公私合作伙伴关系。 该过程将确保设计未来的NSF领导级计算设施，该计算设施融合了最有生产力的近期技术，并预测所有科学和工程的最可能未来技术能力，需要领导级计算和数据分析能力。 此外，该项目有望为领导级计算和数据驱动的应用程序开发新的专业知识和技术，这些应用程序将通过出版物，培训和咨询来使未来用户受益。 该项目将利用团队的独特教育，外展和培训活动的方式来鼓励，教育和发展下一代领导力级计算科学研究人员。该团队包括校园桥接，少数派服务学院（MSI）的领导者以及数据技术，他们将负责使用领域的领导力来使用领导级计算来提高群体的多样性，以进行传统和数据驱动的应用程序。该奖项反映了NSF的法定使命，并通过使用基金会的智力效果进行评估，并以评估范围进行了评估。

项目成果

OMB-Py: Python Micro-Benchmarks for Evaluating Performance of MPI Libraries on HPC Systems

OMB-Py：用于评估 HPC 系统上 MPI 库性能的 Python 微基准

• DOI: 10.1109/ipdpsw55747.2022.00143
• 发表时间: 2022
• 期刊: 23rd Parallel and Distributed Scientific and Engineering Computing Workshop (PDSEC
• 作者: Alnaasan, Nawras;Jain, Arpan;Shafi, Aamir;Subramoni, Hari;Panda, Dhabaleswar K
• 通讯作者: Panda, Dhabaleswar K

Designing Hierarchical Multi-HCA Aware Allgather in MPI

• DOI: 10.1145/3547276.3548524
• 发表时间: 2022-08
• 期刊: Workshop Proceedings of the 51st International Conference on Parallel Processing
• 作者: Tu Tran;Benjamin Michalowicz;B. Ramesh;H. Subramoni;A. Shafi;D. Panda

Highly Efficient Alltoall and Alltoallv Communication Algorithms for GPU Systems

适用于 GPU 系统的高效 Alltoall 和 Alltoallv 通信算法

• DOI: 10.1109/ipdpsw55747.2022.00014
• 发表时间: 2022
• 期刊: Heterogeneity in Computing Workshop
• 作者: Chen, Chen-Chun;Khorassani, Kawthar Shafie;Anthony, Quentin G.;Shafi, Aamir;Subramoni, Hari;Panda, Dhabaleswar K.
• 通讯作者: Panda, Dhabaleswar K.

Hy-Fi: Hybrid Five-Dimensional Parallel DNN Training on High-Performance GPU Clusters

Hy-Fi：高性能 GPU 集群上的混合五维并行 DNN 训练

• DOI: 10.1007/978-3-031-07312-0_6
• 发表时间: 2022
• 期刊: Proceedings International Conference on High Performance Computing
• 作者: Jain, A;Shafi, A.;Anthony, Q.;Kousha, P.;Subramoni, H.;Panda, DK.
• 通讯作者: Panda, DK.

Arm meets Cloud: A Case Study of MPI Library Performance on AWS Arm-based HPC Cloud with Elastic Fabric Adapter

Arm 与云的结合：基于 AWS Arm 的 HPC 云（具有 Elastic Fabric Adapter）的 MPI 库性能案例研究

• DOI: 10.1109/ipdpsw55747.2022.00083
• 发表时间: 2022
• 期刊: IEEE International Parallel and Distributed Processing Symposium Workshops
• 作者: Xu, Shulei;Shafi, Aamir;Subramoni, Hari;Panda, Dhabaleswar K.
• 通讯作者: Panda, Dhabaleswar K.