In this paper we examine the performance of parallel approximate inverse preconditioning for solving finite element systems, using a variety of clusters containing the Message Passing Interface (MPI) communication library, the Globus toolkit and the Open MPI open-source software. The techniques outlined in this paper contain parameters that can be varied so as to tune the execution to the underlying platform. These parameters include the number of CPUs, the order of the linear system (n) and the "retention parameter" (未l) of the approximate inverse used as a preconditioner. Numerical results are presented for solving finite element sparse linear systems on platforms with various CPU types and number, different compilers, different File System types, different MPI implementations and different memory sizes.
Administration of Grid resources is a time consuming and often tedious job. Most administrative requests are predictable, and in general, handling them requires knowledge of the local resources and the requester. In this paper we discuss a system to provide automated support for administrative requests, such as resource reservation and user account management. We propose using trust metrics to help judge the merits and suitability of each request. We outline how these metrics can be implemented using trust management techniques into a practical system we call GridAdmin.
Administration of Grid resources is a time consuming and often tedious job. Most administrative requests are predictable, and in general, handling them requires knowledge of the local resources and the requester. In this paper we discuss a system to provide automated support for administrative requests, such as resource reservation and user account management. We propose using trust metrics to help judge the merits and suitability of each request. We outline how these metrics can be implemented using trust management techniques into a practical system we call GridAdmin.
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