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: 2316234
• 负责人: Md Ariful Azad
• 金额: $ 54.74万
• 依托单位: Indiana University
• 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=2316234&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上的大型图形问题的执行，例如在数据中心中发现的问题。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），社会网络分析问题和软件系统的验证（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，本项目将加速这些子计算。研究人员将确定新的方法来执行这些更有效的子计算，并将构思新的计算硬件，可以更快地执行它们。我们的社会将受益，因为这项工作将使解决这些问题的更大版本更快，更少的能源消耗。此外，该项目还包括一项教育计划，将向高中、大学本科生和研究生教授计算机科学，重点是来自弱势背景的学生。考虑的图形问题的挑战源于所使用的算法的复杂性和许多图形问题的大计算和存储要求。为了应对这些挑战，该项目追求基于三个相互依赖的主要目标的雄心勃勃的跨层努力：用于图形问题的新算法，用于有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。第一个推力集中在几个高回报的算法方向考虑的应用领域：图聚类在静态和动态设置;图建设，同时保留重要信息;和机器学习（ML）技术的应用。在所有这些方向上，该项目都使用了近似值。在第二个目标中，我们开发一个灵活的编程层，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，其中包含一个用于图形的新的特定领域语言（DSL），用于图形处理的高性能数值库（具有可扩展的稀疏方法），以及一个具有两个使用机器学习（ML）技术的中间表示的智能编译器。在第三个推力中，该项目使用新型硬件加速器在大型分布式机器中加速图形应用程序的执行。该加速器具有高级指令集架构（伊萨），具有在瓦片上执行稀疏矩阵操作的指令。一个智能的自动调谐器软件可以帮助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香槟分校、麻省理工学院和印第安纳州大学的十位教授，他们在几个不同的领域都有专业知识。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。