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

本研究通过开发：1)基于云的数据密集型图算法的并行化，2)在异构云环境中高效调度和执行应用的框架，以及3)指定并行性的分层编程抽象，来探索云平台的应用开发和优化。该工作基于云的性能模型，研究和应用了传统并行计算中的大量技术，并探索了云上应用程序的调度和负载平衡策略。其中包括集中式和分布式的调度和工作窃取方法以及工作分担方法。正在制定在执行涉及数据密集型图形计算的应用程序时评估该框架的方法。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。这项研究使跨公共云和私有云开发数据密集型图形应用程序变得更容易。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为云开发应用程序。以能源信息学领域中出现的图问题和流应用为例来说明这些技术。

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.