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

该项目建议为通过动态配置的非统一带宽链路连接的地理分布式计算集群开发高效的网络和应用感知数据驱动的通信和同步原语。同步和通信原语是任何非平凡并行计算的基础，而集群系统中的性能关键取决于这些原语的效率。我们的目标是在利用应用程序特征的同时，有效地将各种动态变化的网络特征融入建议的解决方案中。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示例包括用于不同应用程序驱动模型的屏障、Eureka和终止同步。数据驱动的通信原语包括多对多广播、多播和多对多传输操作的数据相关形式。拟议研究的实验方面将在一系列高性能计算集群和平台上进行，这些集群和平台分布在普渡大学、伊利诺伊大学芝加哥分校和美国国家科学基金会/国防部赞助的HPC中心，通过互联网中的不同带宽链路相互连接。数据密集型应用程序，如数据挖掘/仓储和多媒体，将被用作研究数据驱动的同步和通信问题的工具。所提出的工作将有助于在集群系统上设计此类并行应用程序。

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.