Collaborative Research: PPoSS: Planning: Towards an Integrated, Full-stack System for Memory-centric Computing
协作研究:PPoSS:规划:面向以内存为中心的计算的集成全栈系统
基本信息
- 批准号:2029014
- 负责人:
- 金额:$ 18.55万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2021
- 资助国家:美国
- 起止时间:2021-01-01 至 2022-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
As the volume of data being processed by today’s systems continues to increase, the traditional organization of memory systems is shifting to accommodate that accelerating growth. Data-centric applications such as irregular graph-mining algorithms, distributed machine learning, and genome sequencing require a large amount of data to compute and store, and generate massive amounts of intermediate data to move around the compute resources. Memory-centric computing is a potential solution to overcome the performance bottleneck of current systems. Near or in-memory computing can mitigate the bandwidth limitations with fewer data movements between the memory and host processing units; a remote memory pool with a fast interconnect shared by all processing units can overcome the current capacity constraints. Both solutions are promising for breaking down the memory wall. However, it is challenging to release the power of both solutions with direct integration. In this project, the investigators propose an integrated, full-stack system to enable memory-centric computing (SMC2). The system will incorporate the emerging near-memory data processors (NDP) and an extensible remote memory pool to minimize the performance impact of memory accesses in graph-mining applications. The research tasks include optimizations in architecture, the software/hardware interface, programming models/compilers, and performance models/optimization. First, the architecture is revisited to utilize the NDP hardware to build an active memory system that supports intelligent data prefetch and speculative data push. Next, the system software is redesigned to support NDP function calls, data-push operations, and virtualization. Then, with new system abstractions, a new programming model is proposed to allow programmers to specify which tasks can run on the NDP resources, and to support efficient NDP-to-NDP communication. Lastly, a new system performance model and optimization framework are incorporated. By putting the four pieces together, the proposed system support can maximize the performance of memory-centric computing with new system abstractions and theories.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.
随着当今系统处理的数据量不断增加,传统的内存系统组织正在发生转变,以适应这种加速的增长。以数据为中心的应用程序,如不规则图挖掘算法、分布式机器学习和基因组测序,需要计算和存储大量数据,并生成大量中间数据以在计算资源中移动。以内存为中心的计算是克服当前系统性能瓶颈的一个潜在解决方案。近内存或内存计算可以减少内存和主机处理单元之间的数据移动,从而缓解带宽限制;具有由所有处理单元共享的快速互连的远程内存池可以克服当前的容量限制。这两种解决方案都有希望打破内存墙。然而,通过直接集成来释放这两种解决方案的功能是具有挑战性的。在这个项目中,研究人员提出了一个集成的全栈系统,以实现以内存为中心的计算(SMC 2)。该系统将结合新兴的近内存数据处理器(NDP)和可扩展的远程内存池,以最大限度地减少图形挖掘应用程序中内存访问对性能的影响。研究任务包括体系结构优化、软件/硬件接口优化、编程模型/编译器优化和性能模型/优化。首先,该架构被重新审视以利用NDP硬件来构建支持智能数据预取和推测性数据推送的主动存储器系统。接下来,重新设计系统软件以支持NDP函数调用、数据推送操作和虚拟化。然后,新的系统抽象,提出了一个新的编程模型,允许程序员指定哪些任务可以运行在NDP资源,并支持有效的NDP到NDP通信。最后,一个新的系统性能模型和优化框架。通过将这四个部分结合在一起,拟议的系统支持可以最大限度地提高以内存为中心的计算的性能,并采用新的系统抽象和理论。该奖项反映了NSF的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。
项目成果
期刊论文数量(8)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
SampleMine: A Framework for Applying Random Sampling to Subgraph Pattern Mining through Loop Perforation
- DOI:10.1145/3559009.3569658
- 发表时间:2022-10
- 期刊:
- 影响因子:0
- 作者:Peng Jiang;Yihua Wei;Jiya Su;Rujia Wang;Bo Wu
- 通讯作者:Peng Jiang;Yihua Wei;Jiya Su;Rujia Wang;Bo Wu
CARE: A Concurrency-Aware Enhanced Lightweight Cache Management Framework
- DOI:10.1109/hpca56546.2023.10071125
- 发表时间:2023-02
- 期刊:
- 影响因子:0
- 作者:Xiaoyang Lu;Rujia Wang;Xian-He Sun
- 通讯作者:Xiaoyang Lu;Rujia Wang;Xian-He Sun
PS-ORAM: efficient crash consistency support for oblivious RAM on NVM
- DOI:10.1145/3470496.3527425
- 发表时间:2020-11
- 期刊:
- 影响因子:0
- 作者:Gang Liu;KenLi Li;Zheng Xiao;Rujia Wang
- 通讯作者:Gang Liu;KenLi Li;Zheng Xiao;Rujia Wang
Exploring PIM Architecture for High-Performance Graph Pattern Mining
- DOI:10.1109/lca.2021.3103665
- 发表时间:2021-07
- 期刊:
- 影响因子:2.3
- 作者:Jiya Su;Linfeng He;Peng Jiang;Rujia Wang
- 通讯作者:Jiya Su;Linfeng He;Peng Jiang;Rujia Wang
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
- 期刊:
- 影响因子:0
- 作者:Lu, Xiaoyang;Wang, Rujia;Sun, Xian-He
- 通讯作者:Sun, Xian-He
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Rujia Wang其他文献
Predicting Hemorrhage Progression in Deep Intracerebral Hemorrhage: A Multicenter Retrospective Cohort Study
- DOI:
10.1016/j.wneu.2022.11.022 - 发表时间:
2023-02-01 - 期刊:
- 影响因子:
- 作者:
Lei Song;Hang Zhou;Tingting Guo;Xiaoming Qiu;Dongfang Tang;Liwei Zou;Yu Ye;Yufei Fu;Rujia Wang;Longsheng Wang;Huaqing Mao;Yongqiang Yu - 通讯作者:
Yongqiang Yu
Mixed infection of Bartonella and Eperythrozoon in a dog - case report
犬巴尔通体和附红细胞体混合感染一例报告
- DOI:
10.1590/1678-4162-12955 - 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Nihui Shao;Zihan Xue;Rujia Wang;Yonggang Ma;D. Fang;Hui Zou - 通讯作者:
Hui Zou
CRISPR-Cas System for RNA Detection and Imaging
- DOI:
10.1007/s40242-019-0030-5 - 发表时间:
2019-12-14 - 期刊:
- 影响因子:3.000
- 作者:
Siyu Chen;Rujia Wang;Chunyang Lei;Zhou Nie - 通讯作者:
Zhou Nie
Visual physiological characteristics recognition method of road traffic safety driving behavior
道路交通安全驾驶行为视觉生理特征识别方法
- DOI:
- 发表时间:
- 期刊:
- 影响因子:0
- 作者:
Rujia Wang;Aidi Yi - 通讯作者:
Aidi Yi
Unified wave equation and numerical simulation of mechanical wave propagation in alloy solidification
合金凝固过程中机械波传播的统一波动方程及数值模拟
- DOI:
10.1177/0037549718774842 - 发表时间:
2019 - 期刊:
- 影响因子:0
- 作者:
Rujia Wang;Shiping Wu;Wei Chen - 通讯作者:
Wei Chen
Rujia Wang的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
相似国自然基金
Research on Quantum Field Theory without a Lagrangian Description
- 批准号:24ZR1403900
- 批准年份:2024
- 资助金额:0.0 万元
- 项目类别:省市级项目
Cell Research
- 批准号:31224802
- 批准年份:2012
- 资助金额:24.0 万元
- 项目类别:专项基金项目
Cell Research
- 批准号:31024804
- 批准年份:2010
- 资助金额:24.0 万元
- 项目类别:专项基金项目
Cell Research (细胞研究)
- 批准号:30824808
- 批准年份:2008
- 资助金额:24.0 万元
- 项目类别:专项基金项目
Research on the Rapid Growth Mechanism of KDP Crystal
- 批准号:10774081
- 批准年份:2007
- 资助金额:45.0 万元
- 项目类别:面上项目
相似海外基金
Collaborative Research: PPoSS: Large: A Full-stack Approach to Declarative Analytics at Scale
协作研究:PPoSS:大型:大规模声明性分析的全栈方法
- 批准号:
2316161 - 财政年份:2023
- 资助金额:
$ 18.55万 - 项目类别:
Continuing Grant
Collaborative Research: PPoSS: LARGE: Research into the Use and iNtegration of Data Movement Accelerators (RUN-DMX)
协作研究:PPoSS:大型:数据移动加速器 (RUN-DMX) 的使用和集成研究
- 批准号:
2316176 - 财政年份:2023
- 资助金额:
$ 18.55万 - 项目类别:
Continuing Grant
Collaborative Research: PPoSS: Large: A Full-stack Approach to Declarative Analytics at Scale
协作研究:PPoSS:大型:大规模声明性分析的全栈方法
- 批准号:
2316158 - 财政年份:2023
- 资助金额:
$ 18.55万 - 项目类别:
Continuing Grant
Collaborative Research: PPoSS: LARGE: Cross-layer Coordination and Optimization for Scalable and Sparse Tensor Networks (CROSS)
合作研究:PPoSS:LARGE:可扩展和稀疏张量网络的跨层协调和优化(CROSS)
- 批准号:
2316201 - 财政年份:2023
- 资助金额:
$ 18.55万 - 项目类别:
Standard Grant
Collaborative Research: PPoSS: LARGE: Cross-layer Coordination and Optimization for Scalable and Sparse Tensor Networks (CROSS)
合作研究:PPoSS:LARGE:可扩展和稀疏张量网络的跨层协调和优化(CROSS)
- 批准号:
2316203 - 财政年份:2023
- 资助金额:
$ 18.55万 - 项目类别:
Continuing Grant
Collaborative Research: PPoSS: LARGE: Research into the Use and iNtegration of Data Movement Accelerators (RUN-DMX)
协作研究:PPoSS:大型:数据移动加速器 (RUN-DMX) 的使用和集成研究
- 批准号:
2316177 - 财政年份:2023
- 资助金额:
$ 18.55万 - 项目类别:
Continuing Grant
Collaborative Research: PPoSS: LARGE: Cross-layer Coordination and Optimization for Scalable and Sparse Tensor Networks (CROSS)
合作研究:PPoSS:LARGE:可扩展和稀疏张量网络的跨层协调和优化(CROSS)
- 批准号:
2316202 - 财政年份:2023
- 资助金额:
$ 18.55万 - 项目类别:
Standard Grant
Collaborative Research: PPoSS: LARGE: General-Purpose Scalable Technologies for Fundamental Graph Problems
合作研究:PPoSS:大型:解决基本图问题的通用可扩展技术
- 批准号:
2316235 - 财政年份:2023
- 资助金额:
$ 18.55万 - 项目类别:
Continuing Grant
Collaborative Research: PPoSS: LARGE: Principles and Infrastructure of Extreme Scale Edge Learning for Computational Screening and Surveillance for Health Care
合作研究:PPoSS:大型:用于医疗保健计算筛查和监视的超大规模边缘学习的原理和基础设施
- 批准号:
2406572 - 财政年份:2023
- 资助金额:
$ 18.55万 - 项目类别:
Continuing Grant
Collaborative Research: PPoSS: Large: A Full-stack Approach to Declarative Analytics at Scale
协作研究:PPoSS:大型:大规模声明性分析的全栈方法
- 批准号:
2316159 - 财政年份:2023
- 资助金额:
$ 18.55万 - 项目类别:
Continuing Grant