Collaborative Research: SHF: Medium: Co-optimizing Spectral Algorithms and Systems for High-Performance Graph Learning

合作研究：SHF：中：协同优化高性能图学习的谱算法和系统

• 批准号: 2212370
• 负责人: Zhuo Feng
• 金额: $ 80万
• 依托单位: Stevens Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2212370&HistoricalAwards=false
• 关键词: Collaborative; Research; SHF; Medium; Co

Emerging graph learning techniques have shown promising results for various important applications such as community detection, drug discovery, and electronic design automation (EDA). However, even the state-of-the-art graph learning methods cannot scale to large data sets due to their high algorithm complexity. For example, the latest graph neural network (GNN) algorithms collectively aggregate feature information from the neighborhood of each node, which not only drastically increases the number of computations among nodes but also leads to high memory usage for storing the intermediate results. Hence most graph learning algorithms cannot efficiently handle large-scale problems due to their high computation and storage costs, not to mention the real-world graphs that may involve billions of edges.This project aims at addressing the most pressing challenges in modern graph learning tasks by investigating high-performance spectral graph algorithms and systems based on the latest theoretical breakthroughs. Unlike prior theoretical studies on spectral graph theory that put less focus on practical algorithm implementations and applications, the investigators of this project will develop practically-efficient spectral graph compression algorithms to boost the efficiency and solution quality of existing graph learning methods through algorithm and system co-optimizations by taking advantage of the latest heterogeneous computing devices, such as GPUs, FPGAs, and computational storage devices. The outcome of this project will potentially advance the state of the art in spectral graph theory, machine learning, data analytics, EDA, as well as high-performance computing. This project is also likely to spark new research in other related computer science and engineering fields such as complex system/network modeling, computational biology, precision medicine, and transportation networks. The investigators will partner with the STEM ambassador program at Stevens, and the Diversity Programs in Engineering office at Cornell to recruit highly diversified undergraduate and graduate students to participate in this project, while the latest research results will be integrated into several graduate and upper-division undergraduate courses to prepare a new generation of researchers and practitioners in high-performance machine learning.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.

新兴的图表学习技术已显示出各种重要应用的有希望的结果，例如社区检测，药物发现和电子设计自动化（EDA）。但是，由于其高算法复杂性，即使是最先进的图形学习方法也无法扩展到大型数据集。例如，最新的图形神经网络（GNN）算法从每个节点附近共同汇总特征信息，这不仅大大增加了节点之间的计算数量，而且还会导致存储中间结果的高内存使用情况。因此，大多数图形学习算法由于其高计算和存储成本而无法有效地解决大规模问题，更不用说现实世界中可能涉及数十亿个边缘的现实图表。该项目旨在通过研究基于最新theor theor theor theor theor theor theor theor theor theor theor theor theor theor theor theor theor theor bremater theor theor brematenty theor bremater theor brematent theor theor theor theor theor theor theor，旨在解决现代图形学习任务中最紧迫的挑战。与先前对光谱图理论的理论研究较少关注实际算法的实施和应用，该项目的研究人员将开发实际有效的光谱图压缩算法，以通过算法和系统共同构度来提高现有图形学习方法的效率和解决方案质量，并通过使用最新的计算机和计算机，例如GP和FP，例如GP，例如GP，例如GP，例如GP。该项目的结果将有可能在光谱图理论，机器学习，数据分析，EDA以及高性能计算的情况下推动最新技术。该项目还可能引发其他相关的计算机科学和工程领域的新研究，例如复杂的系统/网络建模，计算生物学，精密医学和运输网络。调查人员将与史蒂文斯的STEM大使计划合作，以及康奈尔大学工程办公室的多样性计划，招募高度多样化的本科生和研究生来参加该项目，而最新的研究结果将被纳入几个研究生和高级学科的本科课程中，以准备成员，以准备成员的新代表和实践者，并准备好了一项统计的人。通过使用基金会的知识分子和更广泛影响的评论标准来通过评估来支持。

项目成果

GARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph Neural Networks

• 发表时间: 2022-01
• 期刊: ArXiv
• 作者: Chenhui Deng;Xiuyu Li;Zhuobo Feng;Zhiru Zhang

HyperEF: Spectral Hypergraph Coarsening by Effective-Resistance Clustering

• DOI: 10.1145/3508352.3549438
• 发表时间: 2022-10
• 期刊: 2022 IEEE/ACM International Conference On Computer Aided Design (ICCAD)
• 作者: Ali Aghdaei;Zhuo Feng

SF-SGL: Solver-Free Spectral Graph Learning From Linear Measurements

• DOI: 10.1109/tcad.2022.3198513
• 发表时间: 2023-02
• 期刊: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
• 影响因子: 2.9
• 作者: Ying Zhang;Zhiqiang Zhao;Zhuo Feng