CAREER:In-Network Computation Meets Data Persistence

职业：网内计算遇见数据持久性

• 批准号: 2046066
• 负责人: Samira Khan
• 金额: $ 61.26万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-15 至 2026-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2046066&HistoricalAwards=false
• 关键词: CAREER; Network; Computation; Meets; Data

In this current data-centric era, data generated by social media, video sharing applications, swarms of sensors, autonomous cars, etc. is growing exponentially. Unfortunately, as the technology scaling slows down, the semiconductor industry has been facing a major challenge in providing better performance while processing such large datasets. As a result, it is essential to redesign current systems to sustain the demand for computing over exponentially growing datasets. However, the fundamental model of computing has not changed over many decades. In our current Von Neumann model, data sits in the slower persistent storage and it is moved back and forth to faster memory for computation by the processor. This project focuses on enabling a paradigm shift in how data is stored and processed in future systems. Data should be processed close to where data is generated and data should be persisted where it is used. The goal of this work is to fundamentally rethink how persistent states are maintained in data centers and expose the persistency domain in the network for better performance, throughput, and tail latency. This work redesigns programmable switches and smart network interface cards (NICs) to manipulate persistent data in the network.This project enables faster execution of applications that manipulate global persistent state, such as global-scale deep learning training. Machine learning models have a huge societal impact and they are being used to predict cancers, epidemics, etc. This research will potentially impact numerous scientific fields to take a leap forward towards new innovations. The datasets, tools, and techniques developed in this research will be made publicly available to benefit the whole community. The educational part of this project will focus on attracting undergraduate and high-school students towards science and technology through a summer program.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.

在当前以数据为中心的时代，由社交媒体，视频共享应用程序，传感器群，自动驾驶汽车等产生的数据呈指数增长。不幸的是，随着技术扩展的减慢，半导体行业在处理如此大的数据集时面临着更好的性能。结果，必须重新设计当前系统，以维持对指数增长的数据集的计算需求。但是，数十年来，计算的基本模型并没有改变。在我们当前的von Neumann模型中，数据位于较慢的持续存储中，并来回移动以更快地通过处理器计算内存。该项目着重于在未来系统中存储和处理数据的方式进行范式转移。应将数据处理接近生成数据的位置，并应将数据持续使用。这项工作的目的是从根本上重新考虑如何在数据中心保持持久状态，并揭示网络中的持久性域，以获得更好的性能，吞吐量和尾巴潜伏期。这项工作重新设计了可编程开关和智能网络接口卡（NIC），以操纵网络中的持久数据。该项目可以更快地执行操纵全球持久状态的应用程序，例如全球尺度深度学习培训。机器学习模型具有巨大的社会影响，它们被用来预测癌症，流行病等。这项研究可能会影响众多科学领域，以迈向新的创新。这项研究中开发的数据集，工具和技术将公开使用，以使整个社区受益。该项目的教育部分将着重于通过夏季计划吸引本科生和高中生进入科学和技术。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。