AitF: EXPL: Data Management in Domain Wall Memory-based Scratchpad for High Performance Mobile Devices

AitF:EXPL:用于高性能移动设备的基于域墙内存的便签本中的数据管理

基本信息

  • 批准号:
    1535755
  • 负责人:
  • 金额:
    $ 39.99万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2015
  • 资助国家:
    美国
  • 起止时间:
    2015-09-01 至 2021-09-30
  • 项目状态:
    已结题

项目摘要

To achieve scalable performance improvement in mobile devices such as smart phones, it is important to integrate a larger and faster memory. The major constraints on enlarging on-chip memory using traditional memory technologies are size and energy efficiency. Size is an issue because devices must inherently be small to be mobile. Energy efficiency is an issue because battery life is often the limiting factor in the usefulness of mobile devices, and the memory subsystem in mobile devices such as smart phones can consume about a third of the total energy. Because of these limitations, the amount of memory per processor in mobile devices such as smart phones has stayed relatively stable over the last few generations of technologies. The incorporation of the emerging technology of Domain Wall Memory in mobile devices is attractive as it can store more information in less volume than any competing technologies, while simultaneously using little energy, and being nearly as fast as traditional memory technologies.However, Domain Wall Memory has physical properties that are different than traditional memory technologies, and that can potentially impact performance. In particular, Domain Wall Memory is divided into tracks, which like a tape, may only be accessed sequentially. Thus accessing two data items stored far apart within the same track will be a costly, time-consuming operation. Thus obtaining the best possible performance of Domain Wall Memory will require algorithms/solutions that will smartly manage the placement of data items into memory so that the sequential access properties of Domain Wall Memory do not significantly degrade performance. The goal of the project is to design, analyze, test and deploy algorithms/solutions for data allocation problems that will arise with the adoption of Domain Wall Memory in mobile devices. As these data management problems have unique features, the development of algorithms for these problems will likely require the development of new algorithmic design and analysis techniques. Developing a good practical implementation of these algorithms, or implementations inspired by these algorithms, will require the development of a significant understanding of both implementation issues and common instance properties. The project will cross-train students in the area of computer architecture and algorithms. Transfer of technology to the industry will bepursued through collaborations and visits.It is promising to incorporate Domain Wall Memory as a software-controlled scratchpad memory, which has been widely adopted in embedded systems for achieving high performance and energy efficiency. For data items allocated in the same Domain Wall Memory track, accesses start with shifting the target domains below the head, which is similar to accesses to sequential access memory such as tape and hard drives. The overhead to access a data item includes both the read/write overhead and the shift overhead. While the former is constant, the latter depends heavily on how the data items are allocated within the track. Thus optimizing aggregate access time in DWM-SPM largely involves minimizing shifts. The performance of DWM-SPM will be significantly affected by the policies used for: (a) Track management: How the data items are organized/ordered within a track, (b) Data layout: How the data items are assigned to tracks, and (c) Data selection: How the data items that will be stored in DWM-SPM are selected.The project will design and formally analyze algorithms for track management, data layout, and data selection in idealized models of Domain Wall Memory. The advantage of formal algorithmics is that the formal objective can drive the discovery of non-intuitive algorithms. Concurrent with this theoretical investigation, a simulation environment will be created to test proposed solutions to managing Domain Wall Memory. This includes the assembly of hardware modeling tools, software simulators, and a collection of benchmark programs. The project will develop and implement some simple policies to serve as a baseline to compare against, and obtain test instances representative of those that would arise in practice, and will transfer the theoretical insights into actual implementations. These implementations would then be compared to the baseline and greedy heuristic solutions.
为了在智能手机等移动设备中实现可扩展的性能改进,集成更大、更快的内存非常重要。使用传统存储器技术扩大片上存储器的主要限制是尺寸和能源效率。尺寸是一个问题,因为设备本质上必须很小才能移动。能源效率是一个问题,因为电池寿命通常是移动设备实用性的限制因素,而智能手机等移动设备中的内存子系统消耗的能量约占总能量的三分之一。由于这些限制,智能手机等移动设备中每个处理器的内存量在过去几代技术中保持相对稳定。在移动设备中采用新兴的畴壁存储器技术非常有吸引力,因为与任何竞争技术相比,它可以以更小的体积存储更多的信息,同时消耗很少的能量,并且速度几乎与传统存储器技术一样快。然而,畴壁存储器具有与传统内存技术不同的物理特性,并且可能会影响性能。特别是,畴壁存储器被分为磁道,就像磁带一样,只能顺序访问。因此,访问存储在同一磁道内的两个相距较远的数据项将是一项昂贵且耗时的操作。因此,要获得域墙存储器的最佳性能,将需要能够智能地管理数据项到存储器中的放置的算法/解决方案,以便域墙存储器的顺序访问属性不会显着降低性能。该项目的目标是设计、分析、测试和部署算法/解决方案,以解决在移动设备中采用域墙内存时出现的数据分配问题。由于这些数据管理问题具有独特的特征,因此这些问题的算法的开发可能需要开发新的算法设计和分析技术。开发这些算法的良好实际实现或受这些算法启发的实现将需要对实现问题和常见实例属性有深入的了解。该项目将对计算机架构和算法领域的学生进行交叉培训。将通过合作和访问寻求向业界的技术转移。将Domain Wall Memory合并为软件控制的暂存器存储器是有希望的,它已广泛应用于嵌入式系统中以实现高性能和高能效。对于分配在同一磁畴壁内存磁道中的数据项,访问从将目标域移至磁头下方开始,这类似于对磁带和硬盘等顺序存取存储器的访问。访问数据项的开销包括读/写开销和移位开销。虽然前者是恒定的,但后者在很大程度上取决于数据项在轨道内的分配方式。因此,优化 DWM-SPM 中的聚合访问时间主要涉及最小化偏移。 DWM-SPM 的性能将受到用于以下方面的策略的显着影响: (a) 轨道管理:如何在轨道内组织/排序数据项,(b) 数据布局:如何将数据项分配给轨道,以及(c) 数据选择:如何选择将存储在 DWM-SPM 中的数据项。该项目将设计并正式分析用于磁畴壁存储器理想模型中的轨道管理、数据布局和数据选择的算法。形式化算法的优势在于形式化目标可以驱动非直观算法的发现。在进行理论研究的同时,将创建一个模拟环境来测试所提出的管理域墙内存的解决方案。这包括硬件建模工具、软件模拟器和基准程序集合的组装。该项目将制定和实施一些简单的政策,作为比较的基准,并获得代表实践中出现的测试实例,并将理论见解转化为实际实施。然后将这些实现与基线和贪婪启发式解决方案进行比较。

项目成果

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Kirk Pruhs其他文献

Kirk Pruhs的其他文献

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{{ truncateString('Kirk Pruhs', 18)}}的其他基金

AF: SMALL: Relational Algorithms
AF:小:关系算法
  • 批准号:
    2209654
  • 财政年份:
    2022
  • 资助金额:
    $ 39.99万
  • 项目类别:
    Standard Grant
EAGER: AF:Small: Algorithms for Relational Machine Learning
EAGER:AF:Small:关系机器学习算法
  • 批准号:
    2036077
  • 财政年份:
    2020
  • 资助金额:
    $ 39.99万
  • 项目类别:
    Standard Grant
AF:Small: Algorithmic Management of Heterogeneous Resources
AF:Small:异构资源的算法管理
  • 批准号:
    1907673
  • 财政年份:
    2019
  • 资助金额:
    $ 39.99万
  • 项目类别:
    Standard Grant
AF: Small: Algorithmic Energy Management in New Information Technologies
AF:小:新信息技术中的算法能源管理
  • 批准号:
    1421508
  • 财政年份:
    2014
  • 资助金额:
    $ 39.99万
  • 项目类别:
    Standard Grant
EAGER: A Framework for joint optimization of power management and performance in virtualized, heterogeneous cloud computing environments
EAGER:虚拟化异构云计算环境中电源管理和性能联合优化的框架
  • 批准号:
    1253218
  • 财政年份:
    2012
  • 资助金额:
    $ 39.99万
  • 项目类别:
    Standard Grant
AF: Small: Green Computing Algorithmics
AF:小型:绿色计算算法
  • 批准号:
    1115575
  • 财政年份:
    2011
  • 资助金额:
    $ 39.99万
  • 项目类别:
    Standard Grant
Science of Power Management
电源管理科学
  • 批准号:
    0936386
  • 财政年份:
    2009
  • 资助金额:
    $ 39.99万
  • 项目类别:
    Standard Grant
Algorithmic Support for Power Management
电源管理的算法支持
  • 批准号:
    0830558
  • 财政年份:
    2008
  • 资助金额:
    $ 39.99万
  • 项目类别:
    Continuing Grant
Collaborative Research: Algorithmic Support for Power Aware Computing and Communication
协作研究:功耗感知计算和通信的算法支持
  • 批准号:
    0514058
  • 财政年份:
    2005
  • 资助金额:
    $ 39.99万
  • 项目类别:
    Standard Grant
Algorithmic Support for Temperature Aware Computing and Networking
温度感知计算和网络的算法支持
  • 批准号:
    0448196
  • 财政年份:
    2004
  • 资助金额:
    $ 39.99万
  • 项目类别:
    Standard Grant

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  • 批准号:
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R&D-OTHER R & D-A RES/EXPL DEV
  • 批准号:
    8125388
  • 财政年份:
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  • 资助金额:
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  • 项目类别:
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