Collaborative Research: SHF: Small: Optimization of Memory Architectures: A Foundation Approach

合作研究：SHF：小型：内存架构优化：基础方法

• 批准号: 2008907
• 负责人: Xian-He Sun
• 金额: $ 22.5万
• 依托单位: Illinois Institute of Technology
• 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: 2020-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2008907&HistoricalAwards=false
• 关键词: Collaborative; Research; SHF; Small; Optimization

This project proposes a foundational approach to model-driven performance optimization of memory systems for modern computer architectures, with the development of a set of memory-architecture optimization methods and tools that are theoretically proven and empirically feasible for modern memory architecture design. The outcome of this project will significantly improve the performance modeling and optimization techniques for designing and evaluating memory architectures in modern computer systems, featuring deep and diverse memory-system hierarchies, heterogeneous memory devices, and complex data-intensive applications, including big-data, cloud and data centers and high-performance computing applications. The findings of this project will improve the content of various courses that the PIs teach. This project plans to proactively recruit minority students by taking advantage of the institutional efforts at IIT and especially at FIU, which is a minority-serving institution. This project will align education and outreach activities with an existing research and education center.The growing disparity between CPU and memory speed causes memory accesses to become a severe performance bottleneck in modern computer architectures. Attempts to solving this “memory wall” problem underpin technological innovations in computer-architecture design over the last two and half decades. The objective of the research is to significantly extend prior memory models and create a practical memory-architecture performance-modeling and optimization framework that can capture the combined effects of data locality, data concurrency, access latency, and multi-tier memory architecture for real applications and on real systems. A simulation-driven approach will be developed with elaborate real-system measurements and performance analyses to examine the potential benefits and identify the performance issues of various memory-architecture designs. More specifically, this project will develop along three research directions: (1) developing theoretical and architectural foundations to address both fundamental questions related to the tiered heterogeneous memory architectures and investigate practical aspects of applying the modeling and optimization framework for various memory architectures; (2) performing model-driven memory-architecture design and optimization for specific memory architectures, including disaggregated memory system, GPU, and deep-memory hierarchy with hybrid memory devices including non-volatile memory; and (3) developing the memory architecture simulator embedded with the performance modeling and optimization framework, and conducting simulation studies and real system measurements to evaluate memory performance, and compare design alternatives and trade-offs.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.

该项目提出了一种针对现代计算机架构的模型驱动的内存系统性能优化的基本方法，开发了一套经过理论验证且在经验上对现代内存架构设计可行的内存架构优化方法和工具。显着改进现代计算机系统中设计和评估内存架构的性能建模和优化技术，具有深度和多样化的内存系统层次结构、异构内存设备和复杂的数据密集型应用程序，包括大数据、云和数据中心该项目的研究结果将改进 PI 教授的各种课程的内容。该项目计划利用 IIT，特别是少数族裔群体的佛罗里达国际大学的机构努力，主动招收少数族裔学生。该项目将使教育和外展活动与现有的研究和教育中心保持一致。CPU 和内存速度之间日益扩大的差距导致内存访问成为现代计算机体系结构中严重的性能瓶颈。支撑技术该研究的目标是显着扩展先前的内存模型，并创建一个实用的内存架构性能建模和优化框架，该框架可以捕获数据局部性、数据的综合影响。将通过详细的真实系统测量和性能分析来开发模拟驱动的方法，以检查潜在的好处并识别各种内存的性能问题。更具体地说，该项目将沿着三个研究方向发展：（1）开发理论和架构基础，以解决与分层异构内存架构相关的基本问题，并实际研究将建模和优化框架应用于各种内存架构的方面；（2）执行模型； - 针对特定内存架构的驱动内存架构设计和优化，包括分解内存系统、GPU 和具有混合内存设备（包括非易失性内存）的深度内存层次结构；以及 (3) 开发嵌入性能建模的内存架构模拟器；和优化框架，并进行模拟研究和真实系统测量来评估内存性能，并比较设计方案和权衡。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查进行评估，被认为值得支持标准。

项目成果

A Generalized Model for Modern Hierarchical Memory System

现代分层存储系统的通用模型

• DOI: 10.1109/wsc57314.2022.10015298
• 发表时间: 2022-12
• 期刊: 2022 Winter Simulation Conference (WSC
• 作者: Najafi, Hamed;Liu, Jason;Lu, Xiaoyang;Sun, Xian
• 通讯作者: Sun, Xian

Premier: A Concurrency-Aware Pseudo-Partitioning Framework for Shared Last-Level Cache

Premier：用于共享末级缓存的并发感知伪分区框架

• DOI: 10.1109/iccd53106.2021.00068
• 发表时间: 2021-10
• 期刊: IEEE 39th International Conference on Computer Design (ICCD
• 作者: Lu, Xiaoyang;Wang, Rujia;Sun, Xian
• 通讯作者: Sun, Xian

The Memory-Bounded Speedup Model and Its Impacts in Computing

内存限制加速模型及其对计算的影响

• DOI: 10.1007/s11390-022-2911-1
• 发表时间: 2023-02
• 期刊: Journal of Computer Science and Technology
• 影响因子: 1.9
• 作者: Sun, Xian;Lu, Xiaoyang
• 通讯作者: Lu, Xiaoyang

APAC: An Accurate and Adaptive Prefetch Framework with Concurrent Memory Access Analysis

APAC：具有并发内存访问分析功能的准确自适应预取框架

• DOI: 10.1109/iccd50377.2020.00048
• 发表时间: 2020-10
• 期刊: 38th IEEE International Conference on Computer Design (ICCD 2020
• 作者: Lu, Xiaoyang;Wang, Rujia;Sun, Xian
• 通讯作者: Sun, Xian

A Study on Modeling and Optimization of Memory Systems

内存系统建模与优化研究

• DOI: 10.1007/s11390-021-0771-8
• 发表时间: 2021-01
• 期刊: Journal of Computer Science and Technology
• 影响因子: 1.9
• 作者: Liu, Jason;Espina, Pedro;Sun, Xian
• 通讯作者: Sun, Xian