Data Driven Communication and Synchronization in Non-Uniform Bandwidth Computing Clusters

该项目建议开发高效的网络和应用程序感知的数据驱动的通信和同步原语的地理分布的计算集群连接在动态配置的非均匀带宽链路。 同步和通信原语是任何非平凡并行计算的骨干，在集群系统中的性能关键取决于这些原语的效率。 我们的目标是有效地将多样化和动态变化的网络特性纳入到建议的解决方案中，同时利用应用程序的特点。 这些解决方案将基于检测和限制可避免的拥堵和热点的方法。 所采用的技术将包括收集全球信息、抽样和随机化。 要研究的同步原语的例子包括屏障，尤里卡，和不同的应用程序驱动的模型的终止同步。 数据驱动的通信原语包括依赖于数据的多对多广播、多播和多对多传输操作。 实验方面的拟议研究将进行的高性能计算集群和平台分布在普渡大学，伊利诺伊大学芝加哥，和NSF/国防部赞助的HPC中心，通过互联网上不同的带宽链接的集合。 数据挖掘/仓储和多媒体等数据密集型应用程序将被用作研究数据驱动的同步和通信问题的工具。 拟议的工作将促进集群系统上的这种并行应用程序的设计。

This project proposes to develop efficient network- and application-aware data-driven communication and synchronization primitives for geographically distributed computing clusters connected over dynamically configured non-uniform bandwidth links. Synchronization and communication primitives are the backbone of any non trivial parallel computation and performance in cluster systems crucially depends on the efficiency of such primitives. The goal is to effectively incorporate diverse and dynamically varying network characteristics into the proposed solutions while taking advantage of the application characteristics. The solutions will be based on methods that detect and limit avoidable congestion and hotspots. The techniques employed will include collecting global information, sampling, and randomization. Examples of synchronization primitives to be investigated include barrier, eureka, and termination synchronizations for different application-motivated models. Data-driven communication primitives include data-dependent forms of many-to-many broadcast, multicast, and many-to-many transport operations. The experimental aspects of the proposed research will be carried out on a collection of high performance computing clusters and platforms spread across Purdue University, University of Illinois at Chicago, and NSF/DOD sponsored HPC centers, interconnected via different bandwidth links in the Internet. Data-intensive applications such as data mining/warehousing and multimedia will be used as a vehicle to study data-driven synchronization and communication issues. The proposed work will facilitate the design of such parallel applications on cluster systems.