CiC (RDDC) Parallelizing Large Scale Graph Problems on the Cloud

CiC (RDDC) 在云端并行化大规模图问题

• 批准号: 1048311
• 负责人: Viktor Prasanna
• 金额: $ 36.99万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-04-01 至 2014-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1048311&HistoricalAwards=false
• 关键词: CiC; RDDC; Parallelizing; Large; Scale

This research explores application development and optimizations for cloud platforms by developing: 1) cloud based parallelization for data intensive graph algorithms, 2) a framework for efficient scheduling and execution of applications in a heterogeneous cloud environment, and 3) hierarchical programming abstraction to specify parallelism. The work investigates and adapts wealth of techniques in traditional parallel computing for graph problems based on a performance model of the cloud and explore strategies for scheduling and load balancing applications on the cloud. These include centralized and distributed approaches for scheduling and work stealing and work sharing. Methodologies to evaluate the framework in executing applications that involve data intensive graph computations are being developed. The broader impact of this project includes addressing key challenges in the areas of application mapping and performance optimization. The research makes developing data intensive graph applications across public and private clouds easier. The developed software will be released as free and open source software to the community, making it possible for researchers and engineers in academia and industry to leverage this work and develop applications for the cloud. Graph problems and streaming applications arising in the area of energy informatics are considered to illustrate the techniques.

本研究通过开发以下内容探索云平台的应用程序开发和优化：1）基于云的数据密集型图形算法并行化，2）异构云环境中应用程序高效调度和执行的框架，以及3）分层编程抽象以指定并行性。 这项工作调查和适应丰富的技术，在传统的并行计算的图形问题的基础上的性能模型的云和探索策略，调度和负载平衡的云上的应用程序。 这些包括用于调度、工作窃取和工作共享的集中式和分布式方法。 正在开发的方法，以评估在执行涉及数据密集型图形计算的应用程序的框架。 该项目的更广泛影响包括解决应用程序映射和性能优化领域的关键挑战。这项研究使得在公共云和私有云上开发数据密集型图形应用程序变得更加容易。开发的软件将作为免费和开源软件向社区发布，使学术界和工业界的研究人员和工程师能够利用这项工作并为云开发应用程序。图问题和流应用中出现的能源信息领域被认为是说明的技术。