IRES Track-1: I/O Research for Data-Intensive Analytics and Deep Learning

IRES Track-1：数据密集型分析和深度学习的 I/O 研究

• 批准号: 1952302
• 负责人: Weikuan Yu
• 金额: $ 30万
• 依托单位: Florida State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1952302&HistoricalAwards=false
• 关键词: IRES; Track; Research; Data; Intensive

Applications of data science are becoming increasingly diverse. These applications include computation, input-output analysis, deep learning and several other fields. These diverse applications tend to generate and process their datasets in very different patterns. Their complex I/O patterns pose numerous challenges due to contention, congestion, and performance variabilities at multiple layers of the I/O stack including I/O middleware libraries, parallel file systems and storage devices. This IRES project aims to organize an international collaboration between Japan and the U.S. for research on I/O performance efficiency and data reliability for data-intensive analytics and deep learning applications. The IRES Track-1 site will be hosted at the Florida State University (FSU), through close collaboration with the RIKEN Center for Computational Science (R-CCS) in Kobe, Japan. As a world-renowned national lab, R-CCS has hosted the fastest K supercomputer in Japan and has been chosen as the site to host Japan’s future exascale computer, Fugaku. This project leverages such facilities for research and training of IRES participants and enriches the portfolio of international collaborations between the U.S. and Japan. Each year for the duration of the project, five (4 graduate and 1 undergraduate) U.S. students will be selected to participate in the IRES program to visit and do research at the R-CCS for 10 weeks. This project pursues cross-layer optimizations on I/O middleware libraries, parallel file systems, and storage configurations, serving data-intensive analytics and deep learning applications. The project consists of a number of research activities, including (1) I/O characterization of large-scale data-intensive applications and parallel file systems on large-scale supercomputers, (2) application-oriented I/O pipelining for deep learning applications and data reduction through compression; (3) user-level cross-layer optimizations of file and storage systems; and (4) development of multi-level checkpoint/restart with optimal checkpoint/restart intervals across hierarchical storage devices. The research can lead to many insights on how to develop efficient and reliable I/O techniques on high-performance computing (HPC) systems. The experience and lessons learned through this research can benefit the development of storage systems on leadership HPC systems for data analytics and deep learning applications and is expected to enhance the professional development of participating students.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.

数据科学的应用正变得越来越多样化。这些应用包括计算、投入产出分析、深度学习等几个领域。这些不同的应用程序往往以非常不同的模式生成和处理它们的数据集。由于I/O堆栈的多个层(包括I/O中间件库、并行文件系统和存储设备)的争用、拥塞和性能差异，它们的复杂I/O模式带来了许多挑战。该IRES项目旨在组织日本和美国之间的国际合作，研究数据密集型分析和深度学习应用程序的I/O性能、效率和数据可靠性。IRES Track-1网站将通过与位于日本神户的理研计算科学中心(R-CCS)的密切合作，在佛罗里达州立大学(FSU)托管。作为世界知名的国家实验室，R-CCS拥有日本最快的K超级计算机，并被选为日本未来艾级计算机Fugaku的所在地。该项目利用这些设施对IRES参与者进行研究和培训，并丰富了美国和日本之间的国际合作组合。在该项目期间，每年将有5名美国学生(4名研究生和1名本科生)被选中参加IRES项目，他们将在R-CCS进行为期10周的访问和研究。该项目致力于I/O中间件库、并行文件系统和存储配置的跨层优化，为数据密集型分析和深度学习应用提供服务。该项目包括一些研究活动，包括(1)大规模数据密集型应用程序和大型超级计算机上的并行文件系统的I/O表征；(2)面向应用程序的I/O流水线，用于深度学习应用程序和通过压缩减少数据；(3)文件和存储系统的用户级跨层优化；以及(4)开发多级检查点/重启，在分层存储设备上具有最佳的检查点/重启间隔。这项研究可以为如何在高性能计算(HPC)系统上开发高效和可靠的I/O技术带来许多见解。通过这项研究获得的经验和教训可以帮助开发用于数据分析和深度学习应用程序的Leadance HPC系统上的存储系统，并有望促进参与学生的专业发展。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

ROBOTune: High-Dimensional Configuration Tuning for Cluster-Based Data Analytics

• DOI: 10.1145/3472456.3472518
• 发表时间: 2021-08
• 期刊: Proceedings of the 50th International Conference on Parallel Processing
• 作者: Md. Muhib Khan;Weikuan Yu

Accurate classification of depression through optimized machine learning models on high-dimensional noisy data

• DOI: 10.1016/j.bspc.2021.103237
• 发表时间: 2022-01
• 期刊: Biomed. Signal Process. Control.
• 作者: Xingang Fang;Julia Klawohn;Alexander De Sabatino;Harsh Kundnani;Jon Ryan;Weikuan Yu;G. Hajcak

O(1) Communication for Distributed SGD through Two-Level Gradient Averaging

• DOI: 10.1109/cluster48925.2021.00054
• 发表时间: 2020-06
• 期刊: 2021 IEEE International Conference on Cluster Computing (CLUSTER)
• 作者: Subhadeep Bhattacharya;Weikuan Yu;Fahim Chowdhury