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: 2316233
• 负责人: Josep Torrellas
• 金额: $ 390万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2028-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2316233&HistoricalAwards=false
• 关键词: Collaborative; Research; PPoSS; LARGE; General

This project seeks to accelerate the execution of large graph problems on large, distributed machines, such as those found in datacenters. The graph computations considered appear in computational biology problems (for example, how species evolved), social network analysis problems, and verification of software systems (for example, how to prove that software is correct). These problems have many basic sub-computations in common, which this project will accelerate. The investigators will identify new ways to perform these sub-computations that are more efficient and will conceive new computing hardware that can execute them faster. Our society will benefit because this work will enable solving bigger versions of these problems faster and with less energy consumption. In addition, the project includes an education program that will teach computer science to high-school, college undergraduate and graduate students, with an emphasis on students from disadvantaged backgrounds. The challenges of the graph problems considered stem from both the complexity of the algorithms used and the large compute and storage requirements of many graph problems. To address these challenges, this projects pursues an ambitious, cross-layer effort based on three interdependent main thrusts: new algorithms for graph problems, a core software framework for the efficient execution of these problems, and heterogeneous hardware to provide acceleration to these problems. The first thrust focuses on a few high-payoff algorithmic directions for the application domains considered: graph clustering in both static and dynamic settings; graph construction while preserving important information; and the application of machine learning (ML) techniques. In all these directions, the project uses approximations. In the second thrust, we develop a flexible programming layer that generates efficient code for a datacenter-scale platform. The project introduces a graph programming framework with a novel Domain-Specific Language (DSL) for graphs, high-performance numerical libraries for graph processing with scalable sparse methods, and a smart compiler with two intermediate representations that uses machine learning (ML) techniques. In the third thrust, the project speeds up the execution of graph applications in a large, distributed machine with a novel hardware accelerator. The accelerator features a high-level Instruction Set Architecture (ISA) with instructions that perform sparse matrix operations on tiles. A smart auto-tuner software helps generate and map code to various accelerators and general-purpose engines. The investigators are ten professors at the University of Illinois Urbana-Champaign, MIT, and Indiana University, with expertise in several distinct areas. The work will be done in close collaboration with industrial research groups.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.

该项目旨在加速大型分布式机器（例如数据中心中的机器）上大型图形问题的执行。所考虑的图计算出现在计算生物学问题（例如，物种如何进化）、社交网络分析问题和软件系统验证（例如，如何证明软件是正确的）中。这些问题有许多共同的基本子计算，该项目将加速这些子计算。研究人员将找到更有效地执行这些子计算的新方法，并将构思出可以更快执行这些子计算的新计算硬件。我们的社会将从中受益，因为这项工作将能够以更少的能源消耗更快地解决这些问题的更大版本。此外，该项目还包括一项向高中生、大学本科生和研究生教授计算机科学的教育计划，重点关注来自弱势背景的学生。所考虑的图问题的挑战源于所使用算法的复杂性以及许多图问题的大量计算和存储要求。为了应对这些挑战，该项目基于三个相互依赖的主要目标进行雄心勃勃的跨层努力：图形问题的新算法、用于高效执行这些问题的核心软件框架以及为这些问题提供加速的异构硬件。第一个重点关注所考虑的应用领域的一些高回报算法方向：静态和动态设置中的图聚类；图构建同时保留重要信息；以及机器学习（ML）技术的应用。在所有这些方向上，该项目都使用近似值。在第二个重点中，我们开发了一个灵活的编程层，可以为数据中心规模的平台生成高效的代码。该项目引入了一个图形编程框架，该框架具有新颖的图形领域特定语言（DSL）、用于具有可扩展稀疏方法的图形处理的高性能数值库，以及具有两种使用机器学习（ML）技术的中间表示的智能编译器。在第三个重点中，该项目通过新型硬件加速器加速了大型分布式机器中图形应用程序的执行。该加速器采用高级指令集架构 (ISA)，其中包含在图块上执行稀疏矩阵运算的指令。智能自动调谐器软件有助于生成代码并将其映射到各种加速器和通用引擎。研究人员是伊利诺伊大学厄巴纳-香槟分校、麻省理工学院和印第安纳大学的十名教授，他们在几个不同领域拥有专业知识。这项工作将与工业研究小组密切合作完成。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力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

Learning Node Abnormality with Weak Supervision

• DOI: 10.1145/3583780.3614950
• 发表时间: 2023-10
• 期刊: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
• 作者: Qinghai Zhou;Kaize Ding;Huan Liu;H. Tong

Geometric Matrix Completion via Sylvester Multi-Graph Neural Network

• DOI: 10.1145/3583780.3615170
• 发表时间: 2022-06
• 期刊: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
• 作者: Boxin Du;Changhe Yuan;Fei Wang;Hanghang Tong