SPX: Collaborative Research: Cross-stack Memory Optimizations for Boosting I/O Performance of Deep Learning HPC Applications

SPX：协作研究：用于提升深度学习 HPC 应用程序 I/O 性能的跨堆栈内存优化

• 批准号: 1919075
• 负责人: Yue Cheng
• 金额: $ 32.06万
• 依托单位: George Mason University
• 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 2023-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1919075&HistoricalAwards=false
• 关键词: SPX; Collaborative; Research; Cross; stack

New computing applications are emerging in smart networks, scientific explorations, business management, security, and healthcare. These applications depend on very large amounts of data. This data must be used in a fast and efficient manner. The use of large supercomputers to analyze such data is on the rise. The techniques they use are referred to as deep learning (DL) high-performance computing (HPC). Researchers are using DL HPC to make sense of this flood of data and obtain useful information. To do this they must redesign HPC systems. A key challenge is how to use resources such as data storage and computer memory at a huge scale. This project will build Metis, a high-performance data storage system that uses new, end-to-end, hardware-supported memory and storage design to meet the needs of DL HPC applications. The goal is to satisfy the challenge posed by increasing data management performance for next-generation supercomputers. The project will connect several different computing communities and increase interactions among them. The project includes educational and engagement activities which will greatly increase the community's understanding of HPC systems. These activities include broadening participation activities to attract and retain new students. Special emphasis will be given to students from underrepresented groups. The project will encourage student interest in design and research in large-scale computing systems design.This project brings together researchers in micro-architecture, distributed computing systems, namely cloud and HPC systems, storage systems, and power/energy modeling to boost DL HPC data processing performance. The research will yield a fundamentally new software-hardware co-designed memory compression technique that transparently compresses DL application memories with negligible runtime performance overhead. Metis will leverage the novel compression substrate to enable a distributed, intelligent, operating-system-level data cache that effectively exploits the physical memory freed via program-memory compression. The developed techniques will open doors for innovative HPC and scientific applications in a broad range of disciplines, which have not been previously possible. Metis' focus on addressing the challenges of increasing performance in the Exascale era, along with engaging researchers from multiple areas, aligns it very well with the goals and objectives of the SPX program. Additionally, the research will also create new knowledge on design principles of memory compression, and yield insights to provide seamless integration of DL applications into the next-generation DL-aware supercomputer infrastructure.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.

新的计算应用正在智能网络、科学探索、商业管理、安全和医疗保健中出现。这些应用程序依赖于非常大量的数据。必须以快速有效的方式使用这些数据。使用大型超级计算机来分析这些数据的情况正在增加。他们使用的技术被称为深度学习（DL）高性能计算（HPC）。研究人员正在使用DL HPC来理解这些数据并获得有用的信息。要做到这一点，他们必须重新设计HPC系统。一个关键的挑战是如何大规模地使用数据存储和计算机内存等资源。该项目将构建Metis，这是一种高性能数据存储系统，使用新的端到端硬件支持的内存和存储设计来满足DL HPC应用程序的需求。其目标是满足下一代超级计算机提高数据管理性能所带来的挑战。该项目将连接几个不同的计算社区，并增加它们之间的互动。该项目包括教育和参与活动，这将大大提高社区对HPC系统的理解。这些活动包括扩大参与活动，以吸引和留住新生。将特别重视代表性不足群体的学生。该项目将鼓励学生对大规模计算系统设计的设计和研究兴趣。该项目汇集了微架构，分布式计算系统，即云和HPC系统，存储系统和功率/能源建模的研究人员，以提高DL HPC数据处理性能。该研究将产生一种全新的软硬件协同设计的内存压缩技术，该技术透明地压缩DL应用程序内存，运行时性能开销可以忽略不计。Metis将利用新的压缩基底来实现分布式、智能、操作系统级的数据缓存，有效地利用通过程序内存压缩释放的物理内存。开发的技术将为创新HPC和广泛学科的科学应用打开大门，这在以前是不可能的。Metis专注于解决Exascale时代提高性能的挑战，沿着来自多个领域的参与研究人员，使其与SPX计划的目标和目的非常一致。此外，该研究还将创造关于内存压缩设计原理的新知识，并产生见解，以提供DL应用程序到下一代DL感知超级计算机基础设施的无缝集成。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

In Search of a Fast and Efficient Serverless DAG Engine

• DOI: 10.1109/pdsw49588.2019.00005
• 发表时间: 2019-10
• 期刊: 2019 IEEE/ACM Fourth International Parallel Data Systems Workshop (PDSW)
• 作者: Benjamin Carver;Jingyuan Zhang;Ao Wang;Yue Cheng

InfiniCache: Exploiting Ephemeral Serverless Functions to Build a Cost-Effective Memory Cache

• 发表时间: 2020-01
• 作者: Ao Wang;Jingyuan Zhang;Xiaolong Ma;Ali Anwar;Lukas Rupprecht;Dimitrios Skourtis;Vasily Tarasov

FaaSNet: Scalable and Fast Provisioning of Custom Serverless Container Runtimes at Alibaba Cloud Function Compute

• 发表时间: 2021-05
• 作者: Ao Wang;Shuai Chang;Huangshi Tian;Hongqi Wang;Haoran Yang;Huiba Li;Rui Du;Yue Cheng

SFS: Smart OS Scheduling for Serverless Functions

• DOI: 10.1109/sc41404.2022.00047
• 发表时间: 2022-09
• 期刊: SC22: International Conference for High Performance Computing, Networking, Storage and Analysis
• 作者: Yuqi Fu;Li Liu;Haoliang Wang;Yue Cheng;Songqing Chen

FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tiers

• DOI: 10.1145/3458817.3476211
• 发表时间: 2020-10
• 期刊: SC21: International Conference for High Performance Computing, Networking, Storage and Analysis
• 作者: Zheng Chai;Yujing Chen;Ali Anwar;Liang Zhao;Yue Cheng;H. Rangwala