The grid: blueprint for a new computing infrastructure
The grid: blueprint for a new computing infrastructure
Scheduling Divisible Loads in Parallel and Distributed Systems
Scheduling Divisible Loads in Parallel and Distributed Systems
Load Balancing Requirements in Parallel Implementations of Image Feature Extraction Tasks
IEEE Transactions on Parallel and Distributed Systems
Sharing Partitionable Workloads in Heterogeneous NOWs: Greedier Is Not Better
CLUSTER '01 Proceedings of the 3rd IEEE International Conference on Cluster Computing
UMR: A Multi-Round Algorithm for Scheduling Divisible Workloads
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
Optimal Algorithms for Scheduling Divisible Workloads on Heterogeneous Systems
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
The Anatomy of the Grid: Enabling Scalable Virtual Organizations
International Journal of High Performance Computing Applications
HPCC'05 Proceedings of the First international conference on High Performance Computing and Communications
A method for scheduling heterogeneous multi-installment systems
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part II
New method for scheduling heterogeneous multi-installment systems
Future Generation Computer Systems
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The performance of parallel computing systems using the master/worker model for distributed grid computing tends to be degraded when large data sets have to be dealt with, due to the impact of data transmission time. In our previous study, we proposed a parallel transferable uniform multi-round algorithm (PTUMR), which efficiently mitigated this impact by allowing chunks to be transmitted in parallel to workers in environments that were homogeneous in terms of workers' computation and communication capacities. The proposed algorithm outperformed the uniform multi-round algorithm (UMR) in terms of application turnaround time, but it could not be directly adapted to heterogeneous environments. In this paper, therefore, we propose an extended version of PTUMR suitable for heterogeneous environments. This algorithm divides workers into appropriate groups based on both computation and communication capacities of individual workers, and then treats each group of workers as one virtual worker. The new PTUMR algorithm is shown through performance evaluations to significantly mitigate the adverse effects of data transmission time between master and workers compared with UMR, achieving turnaround times close to the theoretical lower limits even in heterogeneous environments.