EAGER: An Exaflop-hour simulation in AWS to advance Multi-messenger Astrophysics with IceCube

EAGER：AWS 中的 Exaflop 小时模拟可通过 IceCube 推进多信使天体物理学

• 批准号: 1941481
• 负责人: Frank Wuerthwein
• 金额: $ 29.5万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1941481&HistoricalAwards=false
• 关键词: EAGER; Exaflop; hour; simulation; AWS

Exascale High Throughput Computing (HTC) demonstrates burst capability to advance multi-messenger astrophysics (MMA) with the IceCube detector. At peak, the equivalent of 1.2 Exaflops of 32 bit floating-point compute power is used. This is equivalent to approximately 3 times the scale of the #1 in the Top500 Supercomputer listing as of June 2019. In one hour, roughly 125 terabytes of input data is used to produce 250 terabytes of simulated data that is stored at the University of Wisconsin, Madison to be used to advance IceCube science. This data amounts to about 5% of the annual simulation data produced by the IceCube collaboration in 2018.This demonstration tests and evaluates the ability of HTC-focused applications to effectively utilize availability bursts of Exascale-class resources to produce scientifically valuable output and explores the first 32-bit floating point Exaflop science application. The application concerns IceCube simulations of photon propagation through the ice at its South Pole detector. IceCube is the pre-eminent neutrino experiment for the detection of cosmic neutrinos, and thus an essential part of the MMA program listed among the NSF's 10 Big Ideas.The simulation capacity of the IceCube collaboration is significantly enhanced by efficiently harnessing the power of short-notice Exascale processing capacity at leadership class High Performance Computing systems and commercial clouds. Investigating these capabilities is important to facilitate time-critical follow-up studies in MMA, as well as increasing the overall annual capacity in aggregate by exploiting opportunities for short bursts.The demonstration is powered primarily by Amazon Web Services (AWS) and takes place in the Fall of 2019 during the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC19) in Denver, Colorado. It is a collaboration between the IceCube Maintenance & Operations program and a diverse set of Cyberinfrastructure projects, including the Pacific Research Platform, the Open Science Grid, and HTCondor. By further collaborating with Internet2 and AWS, the experimental project also explores more generally, large high-bandwidth data flows in and out of AWS. The outcomes of this project will thus have broad applicability across a wide range of domains sciences, and scales, ranging from small colleges to national and international scale facilities.This project is supported by the Office of Advanced Cyberinfrastructure in the Directorate for Computer & Information Science & Engineering and the Division of Physics in the Directorate of Mathematical and Physical Sciences.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.

Exascale高吞吐量计算（HTC）展示了利用IceCube探测器推进多信使天体物理学（MMA）的爆发能力。在峰值时，使用相当于1.2 Exaflops的32位浮点计算能力。这相当于截至2019年6月在Top500超级计算机列表中排名第一的规模的约3倍。在一个小时内，大约125 TB的输入数据被用来产生250 TB的模拟数据，这些数据存储在威斯康星州，麦迪逊大学，用于推进冰立方科学。该演示测试和评估了以HTC为重点的应用程序有效利用Exascale级资源的可用性突发来产生有科学价值的输出的能力，并探索了第一个32位浮点Exaflop科学应用。 该应用程序涉及冰立方模拟光子传播通过冰在南极探测器。IceCube是探测宇宙中微子的卓越中微子实验，因此也是美国国家科学基金会（NSF）十大创意之一MMA计划的重要组成部分。IceCube合作项目通过有效利用领导级高性能计算系统和商业云的短通知Exascale处理能力，显著增强了模拟能力。研究这些功能对于促进MMA中时间紧迫的后续研究以及通过利用短脉冲的机会来增加总体年容量非常重要。该演示主要由Amazon Web Services（AWS）提供支持，并将于2019年秋季在丹佛举行的高性能计算、网络、存储和分析国际会议（SC19）期间举行，科罗拉多。这是IceCube维护运营计划与各种网络基础设施项目之间的合作，包括太平洋研究平台，开放科学网格和HTCondor。通过与Internet2和AWS的进一步合作，该实验项目还更广泛地探索了进出AWS的大型高带宽数据流。因此，该项目的成果将在广泛的科学领域和规模上具有广泛的适用性，从小型学院到国家和国际规模的设施。该项目得到了计算机信息科学工程局高级网络基础设施办公室&&和数学与物理科学局物理部的支持。该奖项反映了NSF的法定使命，并被认为是通过使用基金会的知识价值和更广泛的影响审查标准进行评估，

项目成果

Demonstrating 100 Gbps in and out of the public Clouds

演示公有云内外的 100 Gbps

• DOI: 10.1145/3311790.3399612
• 发表时间: 2020
• 期刊: Practice and Experience in Advanced Research Computing (PEARC20
• 作者: Sfiligoi, Igor

Pushing the Cloud Limits in Support of IceCube Science

突破云极限以支持 IceCube Science

• DOI: 10.1109/mic.2020.3045209
• 发表时间: 2021
• 期刊: IEEE Internet Computing
• 影响因子: 3.2
• 作者: Sfiligoi, Igor;Schultz, David;Wurthwein, Frank;Riedel, Benedikt;Deelman, Ewa
• 通讯作者: Deelman, Ewa

Running a Pre-exascale, Geographically Distributed, Multi-cloud Scientific Simulation

• DOI: 10.1007/978-3-030-50743-5_2
• 发表时间: 2020-05-22
• 期刊: High Performance Computing
• 作者: Sfiligoi I;Würthwein F;Riedel B;Schultz D
• 通讯作者: Schultz D

Demonstrating a Pre-Exascale, Cost-Effective Multi-Cloud Environment for Scientific Computing: Producing a fp32 ExaFLOP hour worth of IceCube simulation data in a single workday

演示用于科学计算的前百亿亿次规模、经济高效的多云环境：在单个工作日内生成 fp32 ExaFLOP 小时的 IceCube 模拟数据

• DOI: 10.1145/3311790.3396625
• 发表时间: 2020
• 期刊: PEARC '20: Practice and Experience in Advanced Research Computing
• 作者: Sfiligoi, Igor;Schultz, David;Riedel, Benedikt;Wuerthwein, Frank;Barnet, Steve;Brik, Vladimir
• 通讯作者: Brik, Vladimir