SHF: Small: Software/Hardware Acceleration Architectures for Low-Tail-Latency QoS Provisioning Based Data Centers

SHF：小型：基于低尾延迟 QoS 配置的数据中心的软件/硬件加速架构

• 批准号: 2008975
• 负责人: Xi Zhang
• 金额: $ 40万
• 依托单位: Texas A&M Engineering Experiment Station
• 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: 2020-07-15 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2008975&HistoricalAwards=false
• 关键词: SHF; Small; Software; Hardware; Acceleration; SHF; Small; Software; Hardware; Acceleration

The era of data science is underway, with an explosion of data from social media, environmental monitoring, E-health, national defense, sciences/engineering advances, etc., driving a very fast-growing information-technology sector. As a foundational pillar for big data, data centers play a crucially important role in efficiently collecting, storing, retrieving, classifying, and processing large datasets. In addition, these tremendous volumes of data in data centers, as well as the rapid advances of modern computer techniques, have propelled the ongoing boom of machine learning (ML) from artificial intelligence (AI). While ML aims at automatically learning useful properties from data for accurate and timely stochastic decision making, there is an increasing need for this decision making to occur in a real-time fashion. Thus, one of the most important services in an AI-based interactive data center is how to efficiently process computation-intensive and time-sensitive multimedia (e.g., video, audio) data and provide AI-based decision-making services. However, because of limited computing and storage capabilities, random uncertainties of availability for software/hardware resources, and statistical multiplex switching in data centers, the deterministic delay-bounded requirements for high-volume real-time services of AI-based interactive data-centers are often infeasible. Thus, the PI proposes to extend and apply the statistical delay-bounded quality-of-service (QoS) provisioning theory as an alternative solution to support real-time decision-making services, where the goal is to guarantee bounded delay with a small violation probability, therefore significantly reducing the processing delays currently found in AI-based interactive data-centers. These demand various software/hardware accelerators to be developed to guarantee diverse delay-bounded QoS requirements. The objective of this research is to systematically investigate fundamental and challenging issues on how to extend, apply, and implement the statistical delay-bounded QoS provisioning theory in supporting real-time, interactive, and decision-making services over AI-based interactive data centers. While the statistical delay-bounded QoS provisioning theory has been shown to be a powerful technique and useful performance metric for supporting time-sensitive multimedia transmissions over mobile computing networks, how to efficiently extend and implement this technique/performance-metric for statistically upper-bounding the tail-Latency, which is the worst-case latency dictating delay-bounded QoS performances, imposed in the AI-based interactive data center services has neither been well understood nor thoroughly studied. To overcome the above challenges, employing various emerging computer software/hardware technologies, this project proposes to develop a set of AI-based hybrid software/hardware acceleration architectures, algorithms, and schemes to support the low-tail-latency QoS provisioning for multi-core AI-based interactive data-center services, while reducing the computational workloads and complexities introduced by parallel and distributed data centers. The proposed framework is mainly based on developing novel acceleration architectures for both software and hardware designs and optimizations to significantly boost computing efficiencies through minimizing instruction and data movement and processing across processors and memories. Leveraging the unique novel features and techniques of the statistical delay-bounded QoS provisioning theory and AI-based computing accelerators, a number of QoS-enabling engines constitute the main foundation of this project. More specifically, the research focuses mainly on the following closely coupled research tasks. (1) Develop deep-learning-based processing-in-memory (PIM) systems (PIM QoS-enabling engine) to accelerate training for applications classifications. (2) Develop deep-learning-based application-encoding/aggregating mechanisms and then compare the encoded vectors with trained profiling outputs to classify/aggregate applications. (3) Develop hierarchical cache-partitioning architectures to statistically upper-bound the tail-latency of data-center services by clustering applications based on their load profiles. (4) Develop the precise tail-latency QoS performance-prediction models/metrics and monitoring systems to guarantee the statistical delay-bounded QoS for low tail latency of the higher-priority co-running applications. (5) Develop modeling and analytical techniques, and simulation tools/testbeds, to validate and evaluate the performance for the proposed architectures, frameworks, protocols/algorithms, and schemes. The projects' research intends to benefit the national economy, environment, and society. Also, this project is well integrated with PI’s developments of new graduate and undergrad data-center-relevant curricula/courses at Texas A&M University. The important findings of this project are to be disseminated to the research community through the avenues of journals, conferences, and websites.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.

数据科学的时代正在进行中，随着社交媒体，环境监测，电子健康，国防，科学/工程进步等的数据爆炸，推动了非常快速的信息技术领域。作为大数据的基本支柱，数据中心在有效收集，存储，检索，分类和处理大型数据集方面起着至关重要的作用。此外，这些数据中心中的大量数据以及现代计算机技术的快速进步推动了人工智能（AI）的机器学习（ML）的持续繁荣。尽管ML旨在从数据中自动学习有用的属性以进行准确，及时的随机决策，但这种决策越来越需要实时方式进行。这是基于AI的交互式数据中心中最重要的服务之一是如何有效地处理计算密集型和时间敏感的多媒体（例如，视频，音频）数据并提供基于AI的决策服务。但是，由于计算和存储功能有限，软件/硬件资源可用性的随机不确定性以及数据中心中的统计多重切换，因此基于AI的交互式数据中心的高量实时服务的确定性延迟限制要求通常是可婴儿的。这是PI的提议，旨在扩展和应用统计延迟限制的服务质量（QoS）供应理论，作为支持实时决策服务的替代解决方案，在此目标是通过较小的违规概率来保证有限的延迟，因此大大降低了基于AI的基于AI基的交互式数据境界中当前发现的处理延迟。这些要求开发各种软件/硬件加速器，以保证潜水员延迟束缚的QoS要求。这项研究的目的是系统地调查有关如何扩展，应用和实施统计延迟限制的QoS配置理论的基本问题和挑战问题，以支持基于AI的互动数据中心的实时，互动和决策服务。 While the statistical delay-bound QoS provisioning theory has been shown to be a powerful technique and useful performance metric for supporting time-sensitive multimedia transmissions over mobile computing networks, how to effectively extend and implement this technique/performance-metric for statistically upper-bounding the tail-Latency, which is the worst-case latency dictating delay-bound QoS performances, imposed in the AI-based interactive data center services has neither been well understood nor thoroughly研究。为了克服上述挑战，采用各种新兴的计算机软件/硬件技术，该项目提出了一组基于AI的混合软件/硬件加速体系结构，算法和方案，以支持低尾部latenty QoS提供用于多核AI基于AI的交互式数据中心的多核AI QOS，同时介绍了计算次数和复杂的数据。所提出的框架主要基于为软件和硬件设计开发新颖的加速体系结构以及优化，以通过最大程度地减少处理器和记忆的指导，数据流动以及处理，从而显着提高计算效率。利用统计数据延迟结合的QoS供应理论和基于AI的计算加速器的独特新颖特征和技术，许多实现QoS的发动机构成了该项目的主要基础。更具体地说，该研究主要关注以下紧密耦合的研究任务。 （1）开发基于深度学习的内存处理（PIM）系统（PIM QOS QOS的发动机），以加速用于应用分类的培训。 （2）开发基于深度学习的应用程序编码/聚合机制，然后将编码的向量与训练有素的分析输出进行比较，以分类/汇总应用程序。 （3）通过基于其负载配置文件聚类应用程序来开发层次缓存架构，从而在统计上限制了数据中心服务的尾部延迟。 （4）开发精确的尾部延迟性能预测模型/指标和监视系统，以确保高优先级共运行应用的统计延迟限制QoS。 （5）开发建模和分析技术以及仿真工具/测试床，以验证和评估所提出的体系结构，框架，协议/算法和方案的性能。这些项目的研究旨在使国民经济，环境和社会受益。此外，该项目与PI在德克萨斯A＆M大学的新研究生和本科与数据中心的课程/课程相结合。该项目的重要发现将通过期刊，会议和网站的途径传播给研究界。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子和更广泛的影响审查标准来评估NSF的法定任务。