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GrADSolve - a grid-based RPC system for parallel computing with application-level scheduling

机译:GrADSolve-基于网格的RPC系统,用于具有应用程序级调度的并行计算

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Although some existing Remote Procedure Call (RPC) systems provide support for remote invocation of parallel applications, these RPC systems lack powerful scheduling methodologies for the dynamic selection of resources for the execution of parallel applications. Some RPC systems support parallel execution of software routines with simple modes of parallelism. Some RPC systems statically choose the configuration of resources for parallel execution even before the parallel routines are invoked remotely by the end user. These policies of the existing systems prevent them from being used for remotely solving computationally intensive parallel applications over dynamic computational Grid environments. In this paper, we discuss a RPC system called GrADSolve that supports execution of parallel applications over Grid resources. In GrADSolve, the resources used for the execution of parallel application are chosen dynamically based on the load characteristics of the resources and the characteristics of the application. Application-level scheduling is employed for taking into account both the application and resource properties. GrADSolve also stages the user's data to the end resources based on the data distribution used by the end application. Finally, GrADSolve allows the users to store execution traces for problem solving and use the traces for subsequent solutions. Experiments are presented to prove that GrADSolve's data staging mechanisms can significantly reduce the overhead associated with data movement in current RPC systems. Results are also presented to demonstrate the usefulness of utilizing the execution traces maintained by GrADSolve for problem solving. (C) 2003 Elsevier Inc. All rights reserved.
机译:尽管某些现有的远程过程调用(RPC)系统提供了对并行应用程序的远程调用的支持,但是这些RPC系统缺少强大的调度方法来动态选择用于执行并行应用程序的资源。一些RPC系统通过简单的并行模式支持软件例程的并行执行。一些RPC系统甚至在最终用户远程调用并行例程之前,都会静态选择用于并行执行的资源配置。现有系统的这些策略阻止它们用于在动态计算网格环境上远程解决计算密集型并行应用程序。在本文中,我们讨论了一个名为GrADSolve的RPC系统,该系统支持在Grid资源上执行并行应用程序。在GrADSolve中,根据资源的负载特性和应用程序的特性动态选择用于执行并行应用程序的资源。应用程序级调度用于同时考虑应用程序和资源属性。 GrADSolve还会根据最终应用程序使用的数据分布将用户数据分段到最终资源。最后,GrADSolve允许用户存储执行跟踪以解决问题,并将这些跟踪用于后续解决方案。实验表明,GrADSolve的数据登台机制可以显着减少当前RPC系统中与数据移动相关的开销。还提供了结果,以证明利用GrADSolve维护的执行跟踪来解决问题的有用性。 (C)2003 Elsevier Inc.保留所有权利。

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