CSR: Small: Design and Optimization of Scalable Concurrent Data Structures for Multi-Core Systems

CSR：小型：多核系统可扩展并发数据结构的设计和优化

• 批准号: 1619197
• 负责人: Neeraj Mittal
• 金额: $ 30.19万
• 依托单位: University of Texas at Dallas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1619197&HistoricalAwards=false
• 关键词: CSR; Small; Design; Optimization; Scalable

In the last decade, major general-purpose processor manufacturers have turned to hyper-threading and multi-core architectures to improve hardware performance since many of the traditional approaches for boosting CPU performance have hit a "Brick Wall". Hyper-threading is about running two or more threads in parallel inside a singleCPU. Multi-core is about running two or more actual CPUs on one chip. This multi-core revolution that is currently underway has now moved from general-purpose computing devices such as desktops, laptops and workstations to other more specialized computing devices such as smartphones, tablets, gaming devices, routers and even smartwatches. Unfortunately, most current software applications will not benefit from this enormous parallel processing power offered by a modern computing device unless they are rewritten in a way that enables a program to distribute its tasks across several cores. The future software applications will have to be multi-threaded to achieve any performance gains. A data structure is a fundamental building block of any software program. It is used to manage access to application data and is designed to support specific operations efficiently. In a multi-threaded program, several threads may need to share data and manipulate it concurrently. This gives rise to the problem of designing and building concurrent data structures in which several threads can access the data and manipulate the data structure at the same time, and whose performance scales well with the number of cores. Such high performance concurrent data structures are key to writing multi-threaded programs that scale well with the ever increasing number of cores. In this project, the researcher will develop new techniques for managing contention among concurrent operations that reduce contention window, lower memory footprint, reduce cache traffic and/or decrease traversal overhead. These techniques will be used to develop concurrent version of important data structures suitable for multi-core systems that scale well with the number of cores. The proposed research has the potential of benefiting many areas in computer science and beyond including operating systems, databases, programming languages, game engines and parallel and scientific applications. The PI teaches graduate courses on operating systems and multi-core systems on a regular basis as well as research-oriented seminar courses from time to time. The PI will incorporate many of the results developed during this investigation into graduate courses.

在过去的十年中，由于许多提高CPU性能的传统方法都遇到了“瓶颈”，主要的通用处理器制造商纷纷转向超线程和多核架构来提高硬件性能。 超线程是指在单个 CPU 内并行运行两个或多个线程。多核是指在一个芯片上运行两个或更多实际的 CPU。目前正在进行的这场多核革命现已从台式机、笔记本电脑和工作站等通用计算设备转移到智能手机、平板电脑、游戏设备、路由器甚至智能手表等其他更专业的计算设备。不幸的是，大多数当前的软件应用程序将无法从现代计算设备提供的巨大并行处理能力中受益，除非它们以允许程序将其任务分配到多个内核的方式重写。 未来的软件应用程序必须是多线程的才能实现性能提升。数据结构是任何软件程序的基本构建块。它用于管理对应用程序数据的访问，旨在高效支持特定操作。在多线程程序中，多个线程可能需要共享数据并同时操作数据。 这就产生了设计和构建并发数据结构的问题，其中多个线程可以同时访问数据并操作数据结构，并且其性能随着核心数量的增加而很好地扩展。这种高性能并发数据结构是编写能够随着内核数量不断增加而良好扩展的多线程程序的关键。在该项目中，研究人员将开发用于管理并发操作之间的争用的新技术，以减少争用窗口、降低内存占用、减少缓存流量和/或减少遍历开销。这些技术将用于开发适用于多核系统的重要数据结构的并发版本，这些系统可以随着核心数量的增加而很好地扩展。拟议的研究有可能使计算机科学及其他领域的许多领域受益，包括操作系统、数据库、编程语言、游戏引擎以及并行和科学应用程序。 PI定期教授操作系统和多核系统的研究生课程，并且不时教授研究型研讨会课程。 PI 将把本次调查期间得出的许多结果纳入研究生课程中。