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

本研究通过开发：1)数据密集型图算法的基于云的并行化，2)在异构云环境中高效调度和执行应用程序的框架，以及3)分层编程抽象来指定并行性，探索了云平台的应用程序开发和优化。这项工作基于云的性能模型，研究和调整了传统并行计算中图形问题的大量技术，并探索了在云上调度和负载平衡应用程序的策略。这些方法包括用于调度、工作窃取和工作共享的集中式和分布式方法。在执行涉及数据密集型图形计算的应用程序时，评估框架的方法正在开发中。这个项目更广泛的影响包括解决应用程序映射和性能优化领域的关键挑战。这项研究使跨公共云和私有云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发布给社区，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开发云应用程序。在能源信息学领域中出现的图形问题和流应用被认为说明了这些技术。

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.