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CSR: Small: Collaborative Research: Tuning Extreme-Scale Storage System Through Deep Learning

CSR：小型：协作研究：通过深度学习调整超大规模存储系统

基本信息

批准号：
1817089
负责人：
Forrest Sheng Bao
金额：
$ 23.08万
依托单位：
Iowa State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-10-01 至 2023-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1817089&HistoricalAwards=false
关键词：
CSR Small Collaborative Research Tuning

项目摘要

Many research domains, such as high-energy physics, climate science, astrophysics, combustion science, and computational biology, need to process large amounts of data. Such domains are heavily relying on the capabilities of high performance computing (HPC) systems to manage and efficiently process massive amounts of data. Consequently, applications in the aforementioned research domains require highly optimized performance on the HPC storage systems that store, manage, and manipulate data. This project aims to utilize deep reinforcement learning methods to fine-tune the HPC storage system for optimized performance.This research explores the feasibility of leveraging deep reinforcement learning to optimize HPC storage systems by: (a) Creating a deep learning based HPC storage stack model; (b) Remodeling existing HPC storage stack to support automated configuration and tuning; (c) Collecting training datasets and training the storage stack model; and (d) utilizing the model as a responsive and playable virtual environment to learn the best policy to tune parameters. As a collaborative project, this research aims to advance the domain knowledge of both HPC storage systems and machine learning. The enhanced performance on the HPC storage stack will in turn benefit scientific discovery and thus our society. The investigators will integrate research, education, and outreach efforts during the course of this project, including recruiting and retaining of underrepresented students, mentoring graduate and undergraduate students, integrating research findings into curriculum, and publishing and disseminating results.The data collected to train the storage stack model will be shared at https://discl.cs.ttu.edu/tuningstorage while the code of machine learning at https://github.com/forrestbao/DL4SC. Results and data will be made available by the time of publication. The data will be annotated as appropriate to facilitate interpretation. The principal investigators will strive to maintain the repositories as long as possible.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.

许多研究领域，如高能物理、气候科学、天体物理、燃烧科学和计算生物学，都需要处理大量的数据。这些领域严重依赖于高性能计算（HPC）系统的能力来管理和有效地处理大量数据。因此，上述研究领域中的应用需要在存储、管理和操纵数据的HPC存储系统上高度优化的性能。本项目旨在利用深度强化学习方法对HPC存储系统进行微调，以优化性能。本研究探讨了利用深度强化学习优化HPC存储系统的可行性：（a）创建基于深度学习的HPC存储堆栈模型;（B）重塑现有HPC存储堆栈，以支持自动化配置和调优;（b）创建基于深度强化学习的HPC存储堆栈模型。（c）收集训练数据集并训练存储堆栈模型;以及（d）利用模型作为响应和可播放的虚拟环境来学习调整参数的最佳策略。作为一个合作项目，本研究旨在推进HPC存储系统和机器学习的领域知识。HPC存储堆栈的增强性能将反过来有利于科学发现，从而有利于我们的社会。研究人员将在本项目的过程中整合研究、教育和推广工作，包括招募和留住代表性不足的学生，指导研究生和本科生，将研究成果整合到课程中，以及发布和传播结果。为训练存储堆栈模型而收集的数据将在www.example.com上共享，而机器学习的代码将在https://discl.cs.ttu.edu/tuningstorage上共享。https://github.com/forrestbao/DL4SC结果和数据将在出版时公布。将酌情对数据进行注释，以便于解释。主要研究人员将努力尽可能长时间地维护知识库。该奖项反映了NSF的法定使命，并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，被认为值得支持。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

SueNes: A Weakly Supervised Approach to Evaluating Single-Document Summarization via Negative Sampling

DOI：
10.18653/v1/2022.naacl-main.175
发表时间：
2020-05
期刊：
影响因子：
0
作者：
F. Bao;Hebi Li;Ge Luo;Minghui Qiu;Yinfei Yang;Youbiao He;Cen Chen
通讯作者：
F. Bao;Hebi Li;Ge Luo;Minghui Qiu;Yinfei Yang;Youbiao He;Cen Chen

Two-stage PCB Routing Using Polygon-based Dynamic Partitioning and MCTS

使用基于多边形的动态分区和 MCTS 的两级 PCB 布线

DOI：
10.23919/date56975.2023.10137062
发表时间：
2023
期刊：
Automation & Test in Europe Conference & Exhibition (DATE
影响因子：
0
作者：
He, Youbiao;Li, Hebi;Luo, Ge;Bao, Forrest Sheng
通讯作者：
Bao, Forrest Sheng

DocAsRef: An Empirical Study on Repurposing Reference-based Summary Quality Metrics as Reference-free Metrics

DOI：
10.18653/v1/2023.findings-emnlp.87
发表时间：
2022-12
期刊：
影响因子：
0
作者：
F. Bao;Ruixuan Tu;Ge Luo
通讯作者：
F. Bao;Ruixuan Tu;Ge Luo

RLScheduler: An Automated HPC Batch Job Scheduler Using Reinforcement Learning

DOI：
10.1109/sc41405.2020.00035
发表时间：
2020-11
期刊：
SC20: International Conference for High Performance Computing, Networking, Storage and Analysis
影响因子：
0
作者：
Di Zhang;Dong Dai;Youbiao He;F. Bao;Bing Xie
通讯作者：
Di Zhang;Dong Dai;Youbiao He;F. Bao;Bing Xie

Circuit Routing Using Monte Carlo Tree Search and Deep Reinforcement Learning