Cambridge Service for Data Driven Discovery (CSD3) - A National Data Intensive Science Cloud for Converged Simulation, AI & Analytics

剑桥数据驱动发现服务 (CSD3) - 用于融合模拟、人工智能的国家数据密集型科学云

基本信息

  • 批准号:
    EP/T022159/1
  • 负责人:
  • 金额:
    $ 659.28万
  • 依托单位:
  • 依托单位国家:
    英国
  • 项目类别:
    Research Grant
  • 财政年份:
    2020
  • 资助国家:
    英国
  • 起止时间:
    2020 至 无数据
  • 项目状态:
    未结题

项目摘要

The high-level aims of CSD3 are threefold:- Firstly, to provide the EPSRC community with an accessible, scalable and innovative world-class research facility, with 80% of the system freely available via an open national call. This will enable an increased quantity and diversity of EPSRC researchers, to exploit advanced simulation, data analytics and AI capability, significantly increasing impactful scientific outputs across the entire EPSRC portfolio. Secondly, to kickstart a significant innovation partnership with industry that will investigate emerging exascale technologies, feeding back into the UK-RI HPC roadmap and science programmes, helping to keep the UK at the forefront of HPC & AI technologies. Thirdly, to pro-actively drive a more coordinated Tier-2 network, with greatly improved vertical integration within wider UK-RI Ne-I.To achieve these aims, the proposal builds on the success our previous 2016 Tier-2 proposal where the University of Cambridge (UoC) combined its funds with those from STFC DiRAC and EPSRC Tier-2 to form "CSD3", the UK's largest data intensive national HPC service. CSD3 has seen by far the largest throughput of open-access Tier-2 projects of all current Tier-2 centers. This proposal again levers significant additional non-EPSRC funding, providing 144% match funding from UoC, STFC and industry. These significant investments will be used to enhance the EPSRC Tier-2 funding to expand four key elements of CSD3, integrated with the existing CSD3 to form a significantly enhanced capability.These new service elements are described briefly here: 1) Platform: Retain the highly successful data intensive heterogeneous architecture already deployed and significantly enhance its capacity: Firstly, growing the Intel X86 cluster from 36K cores to 72K. Secondly, create one of the world's first large-scale pre-exascale prototype systems for AI & simulation with exciting next-generation GPUs from NVIDIA doubling the number of GPUs from 360 to 720 but importantly with next generation cards, significantly faster than the current V100 generation. In total, for just £4M, this will increase the EPSRC simulation capability of CSD3 by 4X and its AI capability by 9X. 2) Service support: Substantially increase the user support, RSE, training and Tier-2 management and outreach capability. Also, in collaboration with N8-CIR, Supercomputing Wales, STFC DiRAC & STFC IRIS CSD3 will initiate a new collaboration with 3 key USA HPC centers involved in the US XSEDE HPC program to co-develop, integrate & test a world-leading software ecosystem for allocation management, reporting, impact analysis and improved accessibility of federated e-Infrastructure resources. 3) Accessibility: Improve the user access layer built on Openstack, by creating an ISO27001 certified environment for holding sensitive data, implementing application-specific portals to aid new disciplines and users and develop with an exemplar user community, a community focused scientific gateway, using the gateway tool kit developed at TACC. 4) Open Exascale Lab: An industry funded and jointly resourced partnership to undertake major innovation activity, investigating emerging exascale technologies, across networking, accelerators, programming environments, novel storage and filesystem technologies. Transforming the UK's access to emerging HPC technologies feeding science programs and UK-RI Ne-I roadmap.Thus this proposal will produce significant outputs and impact in 4 areas:- 1)help to deliver impactful EPSRC science across a large number of projects (targeted at 600) and increase UK industrial competitiveness 2)greatly enhanced access to HPC resources, significantly increase training /upskilling for both academic and industrial users 3)greatly enhance and coordinate Tier-2 programme with effort and software tools; 4)undertake significant technology innovation via industry funded Open Exascale La
CSD3的高级目标是三倍: - 首先,为EPSRC社区提供了可访问,可扩展和创新的世界一流研究设施,其中80%的系统可通过公开的国家呼叫免费获得。这将使EPSRC研究人员的数量和多样性增加,从而利用高级模拟,数据分析和AI功能,从而大大增加了整个EPSRC投资组合中有影响力的科学输出。其次,为了与行业建立重大的创新合作伙伴关系,该伙伴关系将调查新兴的Exascale技术,重返英国HPC路线图和科学计划,帮助使英国处于HPC&AI技术的最前沿。第三,为了实现更广泛的英国 - 里ne-i。为了实现这些目标,该提案的垂直整合得到了极大的改进,该提案以我们以前的2016 Tier-2 Tier-2提案的成功为基础,在我们的2016 Tier-2 tier-2提案中,剑桥(UOC)将其资金与STFC Dirac and Epsrc Tier-tier-tier-tier-tier-tore cormenty to cormenty cormentive cormenty corn copt cops cont corn contervers corn cont rar corn corn cornif corn cornive contrive。 CSD3迄今已成为所有当前级别2中心的开放式Tier-2项目的最大吞吐量。该提案再次付出了大量额外的非EPSRC资金,提供了144%的UOC,STFC和行业资金。 These significant investments will be used to enhance the EPSRC Tier-2 funding to expand four key elements of CSD3, integrated with the existing CSD3 to form a significantly enhanced capability.These new service elements are described briefly here: 1) Platform: Retain the highly successful data intensive heterogeneous architecture already deployed and significantly enhance its capacity: Firstly, growing the Intel X86 cluster from 36K cores to 72K.其次,创建世界上第一个用于AI和模拟的大规模前外制造原型系统之一,令人兴奋的NVIDIA的令人兴奋的下一代GPU将GPU的数量从360增加到720,但重要的是,下一代卡片的数量比当前的V100一代要快得多。总共只需400万英镑,这将使CSD3的EPSRC模拟能力增加4倍,其AI能力增加了9倍。 2)服务支持:大大提高用户支持,RSE,培训和2级管理和外展能力。此外,与N8-CIR(超级计算威尔士)合作,STFC DIRAC和STFC IRIS CSD3将与美国XSEDE HPC计划涉及的3个关键的美国HPC计划进行新的合作ISO27001经过认证的环境,用于持有敏感数据,实施特定于应用程序的门户网站来帮助新的学科和用户,并使用TACC开发的Gateway Tool套件,使用示例性的用户社区(一个以社区为中心的科学网关)开发。 4)开放式Exascale实验室:一个基金会和共同报告的行业伙伴关系,以进行重大的创新活动,调查出现Exascale技术,跨越网络,加速器,编程环境,新颖的存储和文件系统技术。改变英国获得新兴的HPC技术喂养科学计划和UK-RI NE-I路线图。该提案将在4个领域产生重大的产出和影响: - 1)帮助在大量项目(目标为600个目标)上提供有影响力的EPSRC科学,并提高英国工业竞争力2)极大地增强了对HPC资源的访问,从而大大提高了学术和工业用户的培训 /提高技能,3)大大增强和协调Tier-Tier-2计划,并使用努力和软件工具进行努力和软件工具; 4)通过行业资助的Open Exascale LA进行重大的技术创新

项目成果

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Paul Alexander其他文献

Direct Optimal Mapping Image Power Spectrum and its Window Functions
直接最优映射图像功率谱及其窗函数
  • DOI:
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Zhilei Xu;Honggeun Kim;J. Hewitt;Kai;N. Kern;Elizabeth Rath;R. Byrne;Adélie Gorce;Z. Martinot;J. Dillon;B. Hazelton;Adrian Liu;M. Morales;Z. Abdurashidova;Tyrone Adams;J. Aguirre;Paul Alexander;Z. Ali;R. Baartman;Yanga Balfour;A. Beardsley;G. Bernardi;T. Billings;J. Bowman;R. Bradley;Philip Bull;J. Burba;S. Carey;C. Carilli;Carina Cheng;D. DeBoer;E. D. L. Acedo;M. Dexter;N. Eksteen;J. Ely;A. Ewall;N. Fagnoni;R. Fritz;S. Furlanetto;K. Gale;B. Glendenning;D. Gorthi;B. Greig;J. Grobbelaar;Z. Halday;J. Hickish;D. Jacobs;A. Julius;M. Kariseb;J. Kerrigan;P. Kittiwisit;S. Kohn;M. Kolopanis;A. Lanman;P. Plante;A. Loots;D. MacMahon;L. Malan;C. Malgas;K. Malgas;B. Marero;A. Mesinger;M. Molewa;Tshegofalang Mosiane;S. Murray;A. Neben;B. Nikolic;H. Nuwegeld;A. Parsons;Nipanjana Patra;Samantha Pieterse;N. Razavi;J. Robnett;K. Rosie;P. Sims;Craig H. Smith;H. Swarts;N. Thyagarajan;P. V. Wyngaarden;P. Williams;Haoxuan Zheng
  • 通讯作者:
    Haoxuan Zheng
Total Recall via Keyqueries: A Case Study for Systematic Reviews
通过键查询进行全面回忆:系统评论的案例研究
  • DOI:
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Paul Alexander
  • 通讯作者:
    Paul Alexander
Private Interactions in Online Discussions
在线讨论中的私人互动
  • DOI:
    10.4018/978-1-7998-3292-8.ch015
  • 发表时间:
    2020
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Lesley Wilton;Rubaina Khan;Clare Brett;Paul Alexander
  • 通讯作者:
    Paul Alexander

Paul Alexander的其他文献

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{{ truncateString('Paul Alexander', 18)}}的其他基金

Proposal for a limited Early Release of Funds to the UK SKA Regional Centre Project
关于提前向英国SKA区域中心项目发放有限资金的提案
  • 批准号:
    ST/X00046X/1
  • 财政年份:
    2022
  • 资助金额:
    $ 659.28万
  • 项目类别:
    Research Grant
Square Kilometre Array: Towards Construction
平方公里阵列:走向建设
  • 批准号:
    ST/T000538/1
  • 财政年份:
    2019
  • 资助金额:
    $ 659.28万
  • 项目类别:
    Research Grant
e-MERLIN Development 2018-2023
e-MERLIN 发展 2018-2023
  • 批准号:
    ST/R001529/1
  • 财政年份:
    2018
  • 资助金额:
    $ 659.28万
  • 项目类别:
    Research Grant
A Common Cloud Platform for STFC Science
STFC Science 的通用云平台
  • 批准号:
    ST/R00255X/1
  • 财政年份:
    2018
  • 资助金额:
    $ 659.28万
  • 项目类别:
    Research Grant
Square Kilometre Array Project
平方公里阵列项目
  • 批准号:
    ST/P006280/1
  • 财政年份:
    2017
  • 资助金额:
    $ 659.28万
  • 项目类别:
    Research Grant
Square Kilometre Array Project 2016/17
平方公里阵列项目2016/17
  • 批准号:
    ST/P005608/1
  • 财政年份:
    2016
  • 资助金额:
    $ 659.28万
  • 项目类别:
    Research Grant
SKA Mid Frequency Aperture Array Test Systems
SKA 中频孔径阵列测试系统
  • 批准号:
    ST/N003683/1
  • 财政年份:
    2016
  • 资助金额:
    $ 659.28万
  • 项目类别:
    Research Grant
Peta-5: A National Facility for Petascale Data Intensive Computation and Analytics
Peta-5:千万亿级数据密集型计算和分析的国家设施
  • 批准号:
    EP/P020259/1
  • 财政年份:
    2016
  • 资助金额:
    $ 659.28万
  • 项目类别:
    Research Grant
Square Kilometre Array Project
平方公里阵列项目
  • 批准号:
    ST/M001393/1
  • 财政年份:
    2013
  • 资助金额:
    $ 659.28万
  • 项目类别:
    Research Grant
PATT Standard T&S Grant 2012 for Professor P Alexander and Dr S J George
PATT标准T
  • 批准号:
    ST/K000225/1
  • 财政年份:
    2012
  • 资助金额:
    $ 659.28万
  • 项目类别:
    Research Grant

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The role of nigrostriatal and striatal cell subtype signaling in behavioral impairments related to schizophrenia
黑质纹状体和纹状体细胞亚型信号传导在精神分裂症相关行为障碍中的作用
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