A proposal for a set of level 3 basic linear algebra subprograms
ACM SIGNUM Newsletter
Matrix Multiplication on Heterogeneous Platforms
IEEE Transactions on Parallel and Distributed Systems
Message Passing: From Parallel Computing to the Grid
Computing in Science and Engineering
A Resource Management Architecture for Metacomputing Systems
IPPS/SPDP '98 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
The internet backplane protocol: a study in resource sharing
Future Generation Computer Systems - Selected papers from CCGRID 2002
A Performance Oriented Migration Framework For The Grid
CCGRID '03 Proceedings of the 3st International Symposium on Cluster Computing and the Grid
Autopilot: Adaptive Control of Distributed Applications
HPDC '98 Proceedings of the 7th IEEE International Symposium on High Performance Distributed Computing
HPDC '02 Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing
Block matrix multiplication in a distributed computing environment: experiments with netsolve
PPAM'05 Proceedings of the 6th international conference on Parallel Processing and Applied Mathematics
| Hi-index | 0.00 |
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.