SHF: Small: High Performance Graph Pattern Mining System and Architecture

SHF：小型：高性能图模式挖掘系统和架构

• 批准号: 2333645
• 负责人: Xuehai Qian
• 金额: $ 50万
• 依托单位: Purdue University
• 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: 2022-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2333645&HistoricalAwards=false
• 关键词: SHF; Small; Performance; Graph; Pattern; SHF; Small; Performance; Graph; Pattern

This research project aims to develop high-performance systems and architectures for graph pattern mining, which the key component for various applications, including mining biochemical structures, finding biological conserved subnetworks, finding functional modules, program control-flow analysis, intrusion network analysis, mining communication graphs, social-network analysis, anomaly detection, and mining XML structures. High-performance graph pattern mining enables fundamental scientific research advance. The research is motivated by the need for scaling to large graphs and patterns; the significant gap between the fastest algorithm and general graph pattern mining systems; and the inefficiency in current computer architectures when executing such workloads. The project vertically advances the field by seeking synergies between algorithm, system, architecture, and hardware implementations. The project provides research opportunities to female, minority and undergraduate students to enhance the broader participation of computer science education. In particular, the project involves non-CS major students, introducing them to graph-analytics techniques to solve problems in science and engineering. This research takes a top-down approach, starting from algorithms and developing efficient graph pattern mining systems and architectures. Based on pattern-decomposition algorithms, it develops efficient and general system mechanisms and compiler optimizations with an accurate cost model. To support distributed graph pattern mining with partitioned graphs, it proposes the idea of breaking down pattern-enumeration algorithms to small tasks with a key abstraction, extendable embedding, and builds an efficient execution model to overlap the communication and computation. At the architecture level, the research proposes novel instruction-set extensions and architectural components to support the stream and intersection operations. The proposed techniques will be implemented in two hardware prototypes: (1) a RISC-V processor with an instruction-set extension for stream and intersection operations; and (2) a distributed FPGA accelerator for graph pattern mining with extendable embedding as the primitive. The research outcomes will be published in top system and architecture conferences. The project will deliver several open-source graph pattern mining systems, architecture simulators and hardware prototypes.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.

该研究项目旨在开发用于图形模式挖掘的高性能系统和架构，这是各种应用程序的关键组成部分，包括采矿生化结构，寻找生物保守的子网，查找功能模块，程序控制流量分析，入侵网络分析，挖掘网络分析，挖掘图形分析，社交网络分析，社交网络分析，Anomaly检测和XMLEST XML结构。高性能图模式采矿使基本的科学研究提前。这项研究是由于需要扩展到大图和模式而进行的。最快的算法和一般图形模式挖掘系统之间的显着差距；以及执行此类工作负载时当前计算机体系结构的效率低下。该项目通过在算法，系统，体系结构和硬件实现之间寻求协同作用来垂直提高该领域。该项目为女性，少数民族和本科生提供了研究机会，以增强计算机科学教育的广泛参与。特别是，该项目涉及非CS主要学生，并将其引入图形分析技术以解决科学和工程学的问题。这项研究采用了自上而下的方法，从算法开始，开发有效的图形模式挖掘系统和体系结构。基于模式分解算法，它通过准确的成本模型开发了高效且一般的系统机制和编译器优化。为了用分区图支持分布式图形模式挖掘，它提出了将模式 - 数字算法分解为使用键抽象，可扩展嵌入的小任务的想法，并构建有效的执行模型以重叠通信和计算。在体系结构层面，研究提出了新颖的教学集扩展和建筑组件，以支持流和交点操作。所提出的技术将在两个硬件原型中实现：（1）带有指令集扩展的RISC-V处理器，用于流和交点操作； （2）用于图形挖掘的分布式FPGA加速器，可扩展嵌入为原始。研究成果将发表在顶级系统和建筑会议上。该项目将提供几种开源图形模式挖掘系统，建筑模拟器和硬件原型。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响评估标准，认为值得通过评估来获得支持。