Eager Maps and Lazy Folds for Graph-Structured Applications

图结构应用程序的 Eager Maps 和 Lazy Folds

• 批准号: 0844500
• 负责人: Suresh Jagannathan
• 金额: $ 45万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-02-01 至 2013-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0844500&HistoricalAwards=false
• 关键词: Eager; Maps; Lazy; Folds; Graph

This project is investigating linguistic extensions to map/reduce abstractions for programming large-scale distributed systems, with special focus on applications that manipulate large, unstructured graphs. It targets real-world graph analysis tasks found in comparative analysis of biological networks as an important case study.The project is investigating the following specific questions: (i) how can highly unstructured graph-based formalisms be cast in the map/reduce framework? (ii) how effectively can these specifications leverage existing map/reduce infrastructure? (iii) how can these abstractions and their execution environments be enhanced to provide the semantic expressiveness necessary for programmability and scalable performance? (iv) how can these analysis tasks be integrated into comprehensive scientific resources usable by the wider applications community? Answers to these questions entails exploring linguistic extensions to existing map/reduce abstractions, defining new implementations on wide-area multicore/SMP platforms, and crafting an expressive graph analysis toolkit suitable for realistic deployment in important domains such as systems biology.Results that arise from this project advance the state-of-the-art in analysis of large sparse unstructured graphs and directly impact a very broad class of scientific applications. Beyond specific target applications in biology, graph-based formalisms find direct applications in social sciences (social networks), recommender systems, and commerce (networks of transactions).

这个项目正在研究语言扩展映射/减少抽象编程大规模分布式系统，特别关注的应用程序，操纵大型，非结构化的图形。 它的目标是在生物网络的比较分析中发现的真实世界的图分析任务作为一个重要的案例研究。该项目正在研究以下具体问题：（i）如何将高度非结构化的基于图的形式化转换到map/reduce框架中？(ii)这些规范如何有效地利用现有的map/reduce基础设施？ (iii)如何增强这些抽象及其执行环境，以提供可编程性和可伸缩性能所需的语义表达能力？(iv)如何将这些分析任务纳入更广泛的应用界可以使用的综合科学资源？这些问题的答案需要探索现有map/reduce抽象的语言扩展，定义广域多核/SMP平台上的新实现，并制作一个富有表现力的图形分析工具包，适用于系统生物学等重要领域的实际部署。该项目的结果推进了艺术在分析大型稀疏非结构化图和直接影响一类非常广泛的科学应用。 除了生物学中的特定目标应用之外，基于图的形式主义还可以直接应用于社会科学（社交网络）、推荐系统和商业（交易网络）。