OAC Core: Scalable Graph ML on Distributed Heterogeneous Systems

OAC 核心：分布式异构系统上的可扩展图 ML

基本信息

批准号：
2209563
负责人：
Viktor Prasanna
金额：
$ 59.97万
依托单位：
University of Southern California
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-15 至 2025-07-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2209563&HistoricalAwards=false
关键词：
OAC Core Scalable Graph ML

项目摘要

Methods that employ graph machine learning (Graph ML), which is a sub-discipline within machine learning that deals with graph data, are becoming important in many key science and engineering domains. For example, the predictive power of graph embedding has been effectively utilized in domains such as social media, biology, pharmacology, and knowledge understanding. However, such methods typically come with an expensive computational footprint, as the computations often need to be performed in real-time on very large and highly heterogeneous static and dynamic graphs with billions of vertices and edges of different types. This project aims at conducting multi-pronged research to enable creation of a cyberinfrastructure (CI) toolkit to run such complex Graph ML applications on emerging heterogeneous distributed systems. The objective of this project is to develop high-performance Graph ML algorithms for key graph workflows spanning multiple scientific and engineering domains targeting distributed heterogeneous systems composed of multi-core processors, Graphics Processing Units (GPUs), Field Programmable Gate Arrays (FPGAs), accelerators and high bandwidth memory interconnected with cache coherent interfaces. The project develops a scalable, deployable, and robust CI toolkit consisting of: (1) novel graph sampling algorithms and efficient Graph ML models for low complexity training and inference computation on static and dynamic graphs; (2) a heterogeneity-aware hardware mapping methodology to accelerate these algorithms and models; and (3) software and hardware libraries for automatic design generation. The project develops proof of concept software for the ML and Data Science communities to facilitate end-to-end deployment of various large-scale applications. Given that graph neural networks are increasingly becoming an important tool for analyzing data in many diverse domains, the outcomes of this project will have a strong impact across a broad range of disciplines, including domains that rely on edge computing, such as autonomous vehicles and smart cities. The project has a robust plan to integrate research into education programs and focuses on activities that promote involvement of students from minority and economically disadvantaged backgrounds into the research.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.

图机器学习（Graph ML）是机器学习中处理图数据的一个子学科，采用图机器学习（Graph ML）的方法在许多关键科学和工程领域变得越来越重要。例如，图嵌入的预测能力已在社交媒体、生物学、药理学和知识理解等领域得到有效利用。然而，此类方法通常需要昂贵的计算足迹，因为计算通常需要在具有数十亿个不同类型的顶点和边的非常大且高度异构的静态和动态图上实时执行。该项目旨在进行多管齐下的研究，以创建网络基础设施 (CI) 工具包，以便在新兴的异构分布式系统上运行此类复杂的 Graph ML 应用程序。该项目的目标是为跨越多个科学和工程领域的关键图工作流程开发高性能图 ML 算法，针对由多核处理器、图形处理单元 (GPU)、现场可编程门阵列 (FPGA) 组成的分布式异构系统，加速器和高带宽内存与高速缓存一致性接口互连。该项目开发了一个可扩展、可部署且强大的 CI 工具包，其中包括：（1）新颖的图采样算法和高效的图 ML 模型，用于静态和动态图的低复杂度训练和推理计算； (2) 一种异构感知硬件映射方法来加速这些算法和模型； (3)用于自动设计生成的软件和硬件库。该项目为机器学习和数据科学社区开发概念验证软件，以促进各种大型应用程序的端到端部署。鉴于图神经网络日益成为分析许多不同领域数据的重要工具，该项目的成果将对广泛的学科产生重大影响，包括依赖边缘计算的领域，例如自动驾驶汽车和智能手机城市。该项目有一个强有力的计划，将研究融入教育计划，并重点关注促进少数族裔和经济弱势背景的学生参与研究的活动。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力评估进行评估，认为值得支持。优点和更广泛的影响审查标准。

项目成果

期刊论文数量（11）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Performance Modeling Sparse MTTKRP Using Optical Static Random Access Memory on FPGA

使用 FPGA 上的光学静态随机存取存储器对稀疏 MTTKRP 进行性能建模

DOI：
10.1109/hpec55821.2022.9926407
发表时间：
2022
期刊：
IEEE High Performance Extreme Computing Conference
影响因子：
0
作者：
Wijeratne, Sasindu;Jaiswal, Akhilesh;Jacob, Ajey P.;Zhang, Bingyi;Prasanna, Viktor
通讯作者：
Prasanna, Viktor

Accelerating Sparse MTTKRP for Tensor Decomposition on FPGA

加速 FPGA 上张量分解的稀疏 MTTKRP

DOI：
10.1145/3543622.3573179
发表时间：
2023
期刊：
ACM/SIGDA International Symposium on Field Programmable Gate Arrays
影响因子：
0
作者：
Wijeratne, Sasindu;Wang, Ta-Yang;Kannan, Rajgopal;Prasanna, Viktor
通讯作者：
Prasanna, Viktor

HTNet: Dynamic WLAN Performance Prediction using Heterogenous Temporal GNN

DOI：
10.1109/infocom53939.2023.10229047
发表时间：
2023-04
期刊：
IEEE INFOCOM 2023 - IEEE Conference on Computer Communications
影响因子：
0
作者：
Hongkuan Zhou;R. Kannan;A. Swami;V. Prasanna
通讯作者：
Hongkuan Zhou;R. Kannan;A. Swami;V. Prasanna

Modeling the Energy Efficiency of GEMM using Optical Random Access Memory

DOI：
10.1109/hpec55821.2022.9926291
发表时间：
2022-09
期刊：
2022 IEEE High Performance Extreme Computing Conference (HPEC)
影响因子：
0
作者：
Bingyi Zhang;Akhilesh R. Jaiswal;Clynn Mathew;R. T. Lakkireddy;Ajey P. Jacob;Sasindu Wijeratne;V. Prasanna
通讯作者：
Bingyi Zhang;Akhilesh R. Jaiswal;Clynn Mathew;R. T. Lakkireddy;Ajey P. Jacob;Sasindu Wijeratne;V. Prasanna

Graph Neural Network for Accurate and Low-complexity SAR ATR