Multivariate geographic clustering in a metacomputing environment using Globus
SC '99 Proceedings of the 1999 ACM/IEEE conference on Supercomputing
Scalable Loop Self-Scheduling Schemes for Heterogeneous Clusters
CLUSTER '02 Proceedings of the IEEE International Conference on Cluster Computing
Mesh Partitioning for Distributed Systems
HPDC '98 Proceedings of the 7th IEEE International Symposium on High Performance Distributed Computing
MPICH-G2: a Grid-enabled implementation of the Message Passing Interface
Journal of Parallel and Distributed Computing - Special issue on computational grids
A framework for adaptive execution in grids
Software—Practice & Experience
A parallel loop self-scheduling on extremely heterogeneous PC clusters
ICCS'03 Proceedings of the 2003 international conference on Computational science
A performance-based parallel loop scheduling on grid environments
The Journal of Supercomputing
Cost-driven autonomous mobility
Computer Languages, Systems and Structures
Performance-based workload distribution on grid environments
GPC'07 Proceedings of the 2nd international conference on Advances in grid and pervasive computing
A fuzzy neural network based scheduling algorithm for job assignment on computational grids
NBiS'07 Proceedings of the 1st international conference on Network-based information systems
Concurrency and Computation: Practice & Experience
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Loop distribution is one of the most useful techniques to reduce the execution time of parallel applications. Traditionally, loop scheduling algorithms are implemented based on parallel programming paradigms such as MPI. This approximation presents three main disadvantages when applied in a Grid environment, namely: (i) all resources must be simultaneously allocated to begin execution of the application; (ii) it is necessary to restart the whole application when a resource fails; (iii) it is not possible to add new resources to a currently running application. To overcome these limitations, we propose a new approach to implement loop distribution schemes in computational Grids. This approach is implemented using the Distributed Resource Management Application API (DRMAA) standard and the GridWay meta-scheduling framework. The efficiency of this approach to solve the Mandelbrot set problem is analyzed in a Globus-based research testbed.