Fast fully parallel thinning algorithms
CVGIP: Image Understanding
Interfacing Condor and PVM to harness the cycles of workstation clusters
Future Generation Computer Systems - Special issue: resource management in distributed systems
Near-optimal adaptive control of a large grid application
ICS '02 Proceedings of the 16th international conference on Supercomputing
High Performance Cluster Computing: Architectures and Systems
High Performance Cluster Computing: Architectures and Systems
Application Load Imbalance on Parallel Processors
IPPS '96 Proceedings of the 10th International Parallel Processing Symposium
Nimrod: a tool for performing parametrised simulations using distributed workstations
HPDC '95 Proceedings of the 4th IEEE International Symposium on High Performance Distributed Computing
An Enabling Framework for Master-Worker Applications on the Computational Grid
HPDC '00 Proceedings of the 9th IEEE International Symposium on High Performance Distributed Computing
Adaptive Scheduling for Task Farming with Grid Middleware
International Journal of High Performance Computing Applications
Modeling master/worker applications for automatic performance tuning
Parallel Computing - Algorithmic skeletons
| Hi-index | 0.00 |
We investigate the problem arising in scheduling parallel applications that follow a master worker paradigm in order to maximize both resource efficiency and application performance. Based on the results obtained in a previous simulation study, we have derived a self-adjusting strategy that can be used to dynamically adjust the number of processors allocated to the application. The effectiveness of the proposed strategy has been assessed in two different scenarios: first, we implemented and tested this strategy on a cluster of homogeneous workstations. Secondly, we extended the self-adjusting strategy to be applied on heterogeneous clusters. We assessed the effectiveness of our strategy using an image-thinning application as a practical example of master-worker application.