CNS Core: Small: Semeru: A memory-disaggregated managed runtime

CNS 核心：小型：Semeru：内存分解的托管运行时

• 批准号: 2007737
• 负责人: Harry Xu
• 金额: $ 50万
• 依托单位: University of California-Los Angeles
• 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: 2020-09-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2007737&HistoricalAwards=false
• 关键词: CNS; Core; Small; Semeru; memory

Modern computing relies heavily on cloud computing systems. The cloud computing systems are powered by large datacenters. Each datacenter contains thousands of machines deployed across the globe. These datacenters are often equipped with heterogeneous hardware devices designed for different kinds of workloads. The ability to use these many machines and devices efficiently and reliably is key to the quality of the services they may provide, and therefore paramount to cloud computing system providers. Recently, resource disaggregation has been proposed as an effective means to improve datacenter reliability and resource utilization. Resource disaggregation builds each resource type as a standalone resource, called a “blade", and connects different resource blades with a network. The Semeru project develops runtime and operating system (OS) support that allows users to run managed cloud applications efficiently in resource-disaggregated datacenters. Semeru focuses on three major thrusts for improving locality for applications and garbage collection (GC) that manages memory for these applications. First, it develops a unified memory abstraction between the Central Processing Unity (CPU) and memory servers, referred to as the Unified Java Heap. This allows CPU and memory servers to see the same virtual address space for the Java heap. Second, it develops a distributed garbage collector (GC) that offloads part of the GC to memory servers, utilizing their idle and weak compute capability to perform continuous, close-to-data object tracing. Object tracing can significantly improve the GC performance. Third, it co-optimizes the runtime and OS kernel so that the OS can provide efficient swapping support as well as new system calls for the runtime. The Semeru project builds a memory-disaggregated runtime for managed cloud workloads, making it possible to translate low-level hardware advances to visible performance benefits that managed cloud applications can enjoy. The development of the project includes a postdoctoral scholar as well as several graduate and undergraduate students, including students from minority groups. It not only advances the state of the art of resource disaggregation, but also trains current and future computing system developers.The project produces a set of artifacts including technical reports, published papers, as well as code repositories. These artifacts are all made available online at http://www.cs.ucla.edu/~harryxu.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.

现代计算严重依赖云计算系统。云计算系统由大型数据中心提供支持。 每个数据中心都包含部署在地球仪上的数千台机器。这些数据中心通常配备有为不同类型的工作负载而设计的异构硬件设备。 高效可靠地使用这些机器和设备的能力是它们可能提供的服务质量的关键，因此对云计算系统提供商至关重要。最近，资源分解已被提出作为一种有效的手段，以提高数据中心的可靠性和资源利用率。 资源分解将每种资源类型构建为独立的资源，称为“刀片”，并将不同的资源刀片与网络连接。Semeru项目开发了运行时和操作系统（OS）支持，允许用户在资源分散的数据中心中高效地运行托管云应用程序。Semeru专注于提高应用程序的局部性和管理这些应用程序的内存的垃圾收集（GC）的三个主要方面。首先，它在中央处理单元（CPU）和内存服务器之间开发了一个统一的内存抽象，称为统一Java堆。 这允许CPU和内存服务器看到Java堆的相同虚拟地址空间。其次，它开发了一个分布式垃圾收集器（GC），将部分垃圾收集器卸载到内存服务器，利用它们的空闲和弱计算能力来执行连续的、接近数据的对象跟踪。 对象跟踪可以显著提高GC性能。第三，它协同优化运行时和操作系统内核，以便操作系统可以为运行时提供有效的交换支持以及新的系统调用。Semeru项目为托管云工作负载构建了一个内存分散的运行时，使其能够将低级硬件的进步转化为托管云应用程序可以享受的可见性能优势。该项目的开发包括一名博士后学者以及几名研究生和本科生，包括少数群体的学生。它不仅推进了资源分解的艺术状态，而且还培训了当前和未来的计算系统开发人员。该项目产生了一系列工件，包括技术报告，发表的论文以及代码库。这些文物都可以在www.example.com网站上获得http://www.cs.ucla.edu/~harryxu.This奖项反映了NSF的法定使命，并被认为值得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来支持。

项目成果

Satune: synthesizing efficient SAT encoders

• DOI: 10.1145/3428214
• 发表时间: 2020-11
• 期刊: Proceedings of the ACM on Programming Languages
• 作者: Hamed Gorjiara;G. Xu;Brian Demsky

MemLiner: Lining up Tracing and Application for a Far-Memory-Friendly Runtime Authors:

MemLiner：为远程内存友好的运行时排列跟踪和应用程序作者：

• 发表时间: 2022
• 期刊: USENIX Symposium on Operating Systems Design and Implementation
• 作者: Chenxi Wang;Haoran Ma;Shi Liu;Yifan Qiao;Jonathan Eyolfson;Christian Navasca;Shan Lu;Guoqing Harry Xu
• 通讯作者: Guoqing Harry Xu

Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threads

• 发表时间: 2021-05
• 作者: John Thorpe;Yifan Qiao;Jon Eyolfson;Shen Teng;Guanzhou Hu;Zhihao Jia;Jinliang Wei;Keval Vora-Keval-Vor

UPGRADVISOR: Early Adopting Dependency Updates Using Hybrid Program Analysis and Hardware Tracing

UPGRADVISOR：使用混合程序分析和硬件跟踪尽早采用依赖项更新

• 发表时间: 2022
• 期刊: Proceedings of the 16th USENIX Symposium on Operating Systems Design and Implementation (OSDI 2022
• 作者: David, Yaniv;Sun, Xudong;Sofaer, Raphael J.;Senthilnathan, Aditya;Yang, Junfeng;Zuo, Zhiqiang;Xu, Guoqing Harry;Nieh, Jason;Gu, Ronghui
• 通讯作者: Gu, Ronghui

Chianina: an evolving graph system for flow- and context-sensitive analyses of million lines of C code

• DOI: 10.1145/3453483.3454085
• 发表时间: 2021-06
• 期刊: Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation
• 作者: Zhiqiang Zuo;Yiyu Zhang;Qiuhong Pan;S. Lu;Yue Li;Linzhang Wang;Xuandong Li;G. Xu