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SPX: Collaborative Research: Harnessing the Power of High-Bandwidth Memory via Provably Efficient Parallel Algorithms

SPX: Collaborative Research: Harnessing the Power of High-Bandwidth Memory via Provably Efficient Parallel Algorithms
SPX:协作研究:通过可证明高效的并行算法利用高带宽内存的力量
批准号:
1725661
负责人:
Benjamin Moseley
金额:
$25.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2017
资助国家:
美国
项目状态:
已结题
起止时间:
2017-09-15 至 2018-03-31

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中文摘要
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英文摘要
An important bottleneck for many parallel scientific applications is memory performance. Recently, vendors have introduced a new memory called high-bandwidth memory (HBM) as an approach to alleviate this bottleneck. This project will develop an algorithmic foundation for using HBM. The project has the potential for a broad economic, technological, and scientific impact, since industry has an investment in this technology and many of the nation's strategic codes are being run on HBM-capable machines. The PIs will integrate research with education at the graduate and undergraduate levels by training PhD, MS, and honors program BS students in cross-cutting issues encompassing algorithm design, high-performance software, and processor architecture. The new approach offered by vendors is to bond memory (HBM) directly to the processor chip, which allows for more connections, enabling higher bandwidth. Although the size of the new memory is larger than modern on-chip caches, physical constraints limit the capacity of the memory to be significantly smaller than DRAM. HBM does not cleanly fit in the standard memory hierarchy. This project will develop a foundational understanding of how to algorithmically design codes for HBM enhanced architectures. Overcoming these intellectual challenges to achieve multi-core scalability using HBM requires new algorithms, models, and abstractions, spearheaded by this collaboration between researchers who study hardware issues, high performance computing challenges, and theoretical modeling and analysis.
期刊论文(2)
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会议论文
A Scalable Approximation Algorithm for Weighted Longest Common Subsequence
加权最长公共子序列的可扩展近似算法
DOI: 10.1007/978-3-030-85665-6_23
发表时间: 2021
期刊: International European Conference on Parallel and Distributed Computing
影响因子: --
作者: [Buhler, J., Lavastida, T., Lu, K., Moseley, B.]
通讯作者: Moseley, B.
DOI: 10.1145/3294052.3319694
发表时间: 2018-12
期刊: Proceedings of the 38th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems
影响因子: --
作者: [Mahmoud Abo Khamis;Ryan R. Curtin;Benjamin Moseley;H. Ngo;X. Nguyen;Dan Olteanu;Maximilian Schleich]
通讯作者: Mahmoud Abo Khamis;Ryan R. Curtin;Benjamin Moseley;H. Ngo;X. Nguyen;Dan Olteanu;Maximilian Schleich
Collaborative Research: AF: Small: Foundations of Algorithms Augmented with Predictions
  • 批准号:
    2121744
  • 项目类别:
    Standard Grant
  • 资助金额:
    $25.0万
  • 财政年份:
    2022
  • 负责人:
    Benjamin Moseley
  • 依托单位:
CAREER: Pushing the Theoretical Limits of Scalable Distributed Algorithms
  • 批准号:
    1845146
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $50.0万
  • 财政年份:
    2019
  • 负责人:
    Benjamin Moseley
  • 依托单位:
AF: Small: Collaborative Research: Algorithmic and Computational Frontiers of MapReduce for Big Data Analysis
  • 批准号:
    1830711
  • 项目类别:
    Standard Grant
  • 资助金额:
    $11.45万
  • 财政年份:
    2018
  • 负责人:
    Benjamin Moseley
  • 依托单位:
SPX: Collaborative Research: Harnessing the Power of High-Bandwidth Memory via Provably Efficient Parallel Algorithms
  • 批准号:
    1824303
  • 项目类别:
    Standard Grant
  • 资助金额:
    $25.0万
  • 财政年份:
    2018
  • 负责人:
    Benjamin Moseley
  • 依托单位:
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