Transparent Middleware Support for Shared State on Heterogeneous Clusters

大规模和小规模分布式系统的爆炸性增长导致了大量新应用程序的出现，其中大多数应用程序采用了分布式共享状态的概念：信息需要多个位置。基于pi的经验，本研究将为运行在异构平台上的应用程序开发方便、高效的中间件。其目标是大大减少开发分布式应用程序所需的程序员工作量，特别是使在本地集群上运行应用程序与在共享内存多处理器上运行应用程序一样容易。因此，建议的研究将采用共享内存编程模型。pi将扩展他们之前在软件分布式共享内存（S-DSM）方面的工作，以适应异构语言和机器类型，并透明地为独立部署的进程提供共享状态。它们将引入特定于应用程序的概念，即何时缓存的数据副本“最近”可以使用，从而促进在更全局分布的环境中优化通信。推动研究的应用包括科学模拟和本地同事在数据挖掘和智能多感官环境领域开发的代码。这项研究将促进这些应用所需资源的有效分配。

The explosive growth of both large and small scale distributed systems is leading to a host of new applications, most of which employ some notion of distributed shared state: information requiredat more than one location. Building on the experience of the PIs, this research will develop convenient, efficient middleware for applications running on heterogeneous platforms. The goal is to dramatically reduce the programmer effort required to develop distributed applications, and in particular to make it as easy to run applications on a local-area cluster as it is to to run them ona shared-memory multiprocessor. The proposed research will therefore employ a shared-memory programming model. The PIs will extend their previous work in software distributed shared memory (S-DSM) to accommodate heterogeneous languages and machine types,and to transparently provide shared state for independently-deployed processes. They will introduce application-specific notions of when a cached copy of data is ``recent enough'' to use, thereby facilitating optimized communication in more globally distributed environments.The applications driving the research include both scientific simulations and codes developed by local colleagues in the areas of datamining and intelligent multi-sense environments.The research will facilitate the effective distribution of these applications across the resources they require.