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CSR: Small: Collaborative Research: Tuning Extreme-scale Storage Stack through Deep Reinforcement Learning

CSR：小型：协作研究：通过深度强化学习调整超大规模存储堆栈

基本信息

批准号：
1817094
负责人：
Yong Chen
金额：
$ 24万
依托单位：
Texas Tech 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 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1817094&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。这类领域严重依赖高性能计算(HPC)系统的能力来管理和高效处理海量数据。因此，上述研究领域中的应用程序需要在存储、管理和操作数据的HPC存储系统上实现高度优化的性能。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。本研究通过以下方式探索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配置和调整；(C)收集训练数据集并训练存储堆栈模型；以及(D)将该模型用作响应和可玩的虚拟环境，以学习调整参数的最佳策略。作为一个协作性项目，本研究旨在提升高性能计算存储系统和机器学习的领域知识。HPC存储堆栈的增强性能反过来将造福于科学发现，从而造福于我们的社会。研究人员将在这个项目的过程中整合研究、教育和推广工作，包括招募和保留代表性不足的学生，指导研究生和本科生，将研究结果整合到课程中，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https://discl.cs.ttu.edu/tuningstorage共享，而机器学习的代码将在https://github.com/forrestbao/DL4SC.共享结果和数据将在发布时公布。数据将作适当的注解，以方便解释。首席调查人员将努力尽可能长时间地维护存储库。这一裁决反映了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。