Synchronization and caching data for numerical linear algebra algorithms in distributed and grid computing environments

Because of the dynamic and heterogeneous nature of a grid infrastructure, the client/server paradigm is a common programming model for these environments, where the client submits requests to several geographically remote servers for executing already deployed applications on its own data. According to this model, the applications are usually decomposed into independent tasks that are solved concurrently by the servers (the so called Data Grid applications). On the other hand, as many scientific applications are characterized by very large set of input data and dependencies among subproblems, avoiding unnecessary synchronizations and data transfer is a difficult task. This work addresses the problem of implementing a strategy for an efficient task scheduling and data management in case of data dependencies among subproblems in the same Linear Algebra application. For the purpose of the experiments, the NetSolve distributed computing environment has been used and some minor changes have been introduced to the underlying Distributed Storage Infrastructure in order to implement the proposed strategies.