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Computation for the Endless Frontier

Computation for the Endless Frontier
无尽前沿的计算
批准号:
1818253
负责人:
Daniel Stanzione
金额:
$6000.0万
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
项目状态:
未结题
起止时间:
2018-09-01 至 2025-02-28

项目摘要

项目成果

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中文摘要
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英文摘要
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.
期刊论文(12)
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科研奖励(0)
会议论文
DOI: 10.1145/3547276.3548524
发表时间: 2022-08
期刊: Workshop Proceedings of the 51st International Conference on Parallel Processing
影响因子: --
作者: [Tu Tran;Benjamin Michalowicz;B. Ramesh;H. Subramoni;A. Shafi;D. Panda]
通讯作者: Tu Tran;Benjamin Michalowicz;B. Ramesh;H. Subramoni;A. Shafi;D. Panda
Network-Assisted Noncontiguous Transfers for GPU-Aware MPI Libraries
GPU 感知 MPI 库的网络辅助非连续传输
DOI: 10.1109/mm.2023.3241133
发表时间: 2023
期刊: IEEE Micro
影响因子: 3.6
作者: [Suresh, Kaushik Kandadi, Khorassani, Kawthar Shafie, Chen, Chen Chun, Ramesh, Bharath, Abduljabbar, Mustafa, Shafi, Aamir, Subramoni, Hari, Panda, Dhabaleswar K.]
通讯作者: Panda, Dhabaleswar K.
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
影响因子: --
作者: [Alnaasan, Nawras, Jain, Arpan, 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
影响因子: --
作者: [Jain, A, Shafi, A., Anthony, Q., Kousha, P., Subramoni, H., Panda, DK.]
通讯作者: Panda, DK.
12
    Final Design Planning for the Leadership-Class Computing Facility
    • 批准号:
      2212090
    • 项目类别:
      Cooperative Agreement
    • 资助金额:
      $350.0万
    • 财政年份:
      2022
    • 负责人:
      Daniel Stanzione
    • 依托单位:
    Characteristic Science Applications for the Leadership Class Computing Facility
    • 批准号:
      2139536
    • 项目类别:
      Cooperative Agreement
    • 资助金额:
      $699.94万
    • 财政年份:
      2021
    • 负责人:
      Daniel Stanzione
    • 依托单位:
    Preliminary Design Planning for the Leadership-Class Computing Facility
    • 批准号:
      2033468
    • 项目类别:
      Cooperative Agreement
    • 资助金额:
      $350.0万
    • 财政年份:
      2020
    • 负责人:
      Daniel Stanzione
    • 依托单位:
    Collaborative Research: Chameleon Phase III: A Large-Scale, Reconfigurable Experimental Environment for Cloud Research
    • 批准号:
      2027176
    • 项目类别:
      Cooperative Agreement
    • 资助金额:
      $300.1万
    • 财政年份:
      2020
    • 负责人:
      Daniel Stanzione
    • 依托单位:
    海外基金