SHF: Large: Collaborative Research: PXGL: Cyberinfrastructure for Scalable Graph Execution

SHF：大型：协作研究：PXGL：可扩展图形执行的网络基础设施

• 批准号: 1111888
• 负责人: Andrew Lumsdaine
• 金额: $ 110万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1111888&HistoricalAwards=false
• 关键词: SHF; Large; Collaborative; Research; PXGL

The most powerful computing systems in the world have historically been dedicated to solving scientific problems. Until recently, the computations performed by these systems have typically been simulations of various physical phenomena. However, a new paradigm for scientific discovery has been steadily rising in importance, namely, data-intensive science, which focuses sophisticated analysis techniques on the enormous (and ever increasing) amounts of data being produced in scientific, commercial, and social endeavors. Important research based on data-intensive science include areas as diverse as knowledge discovery, bioinformatics, proteomics and genomics, data mining and search, electronic design automation, computer vision, and Internet routing. Unfortunately, the computational approaches needed for data-intensive science differ markedly from those that have been so effective for simulation-based supercomputing. To enable and facilitate efficient execution of data-intensive scientific problems, this project will develop a comprehensive hardware and software supercomputing system for data-intensive science.Graph algorithms and data structures are fundamental to data-intensive computations and, consequently, this project is focused on providing fundamental, new understandings of the basics of large-scale graph processing and how to build scalable systems to efficiently solve large-scale graph problems. In particular, this work will characterize processing overheads and the limits of graph processing scalability, develop performance models that properly capture graph algorithms, define the (co-design) process for developing graph-specific hardware, and experimentally verify our approach with a prototype execution environment. Key capabilities of our system include: a novel fine-grained parallel programming model, a scalable library of graph algorithms and data structures, a graph-optimized core architecture, and a scalable graph execution platform. The project will also address the programming challenges involved in constructing scalable and reliable software for data-intensive problems.

世界上最强大的计算系统历来致力于解决科学问题。直到最近，这些系统执行的计算通常都是对各种物理现象的模拟。然而，科学发现的新范式的重要性正在稳步上升，即数据密集型科学，它将复杂的分析技术集中在科学、商业和社会活动中产生的大量（且不断增加的）数据上。基于数据密集型科学的重要研究包括知识发现、生物信息学、蛋白质组学和基因组学、数据挖掘和搜索、电子设计自动化、计算机视觉和互联网路由等多种领域。不幸的是，数据密集型科学所需的计算方法与基于模拟的超级计算非常有效的计算方法明显不同。为了实现和促进数据密集型科学问题的高效执行，该项目将为数据密集型科学开发一个全面的硬件和软件超级计算系统。图算法和数据结构是数据密集型计算的基础，因此，该项目致力于提供对大规模图处理基础知识以及如何构建可扩展系统以有效解决大规模图问题的全新理解。特别是，这项工作将描述处理开销和图形处理可扩展性的限制，开发正确捕获图形算法的性能模型，定义开发图形特定硬件的（协同设计）过程，并使用原型执行环境对我们的方法进行实验验证。我们系统的关键功能包括：新颖的细粒度并行编程模型、可扩展的图算法和数据结构库、图优化的核心架构以及可扩展的图执行平台。该项目还将解决为数据密集型问题构建可扩展且可靠的软件所涉及的编程挑战。