Guided self-scheduling: A practical scheduling scheme for parallel supercomputers
IEEE Transactions on Computers
High Performance Cluster Computing: Architectures and Systems
High Performance Cluster Computing: Architectures and Systems
MPI-The Complete Reference, Volume 1: The MPI Core
MPI-The Complete Reference, Volume 1: The MPI Core
Trapezoid Self-Scheduling: A Practical Scheduling Scheme for Parallel Compilers
IEEE Transactions on Parallel and Distributed Systems
Performance Contracts: Predicting and Monitoring Grid Application Behavior
GRID '01 Proceedings of the Second International Workshop on Grid Computing
Autopilot: Adaptive Control of Distributed Applications
HPDC '98 Proceedings of the 7th IEEE International Symposium on High Performance Distributed Computing
The Grid 2: Blueprint for a New Computing Infrastructure
The Grid 2: Blueprint for a New Computing Infrastructure
Overhead Analysis of a Dynamic Load Balancing Library for Cluster Computing
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 1 - Volume 02
PEMPIs: a new methodology of modeling and prediction of MPI programs performance
International Journal of Parallel Programming
Locality and Loop Scheduling on NUMA Multiprocessors
ICPP '93 Proceedings of the 1993 International Conference on Parallel Processing - Volume 02
A parallel loop self-scheduling on extremely heterogeneous PC clusters
ICCS'03 Proceedings of the 2003 international conference on Computational science
Dynamic multi phase scheduling for heterogeneous cluste
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Cooperative load balancing for a network of heterogeneous computers
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
A dynamic partitioning self-scheduling scheme for parallel loops on heterogeneous clusters
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part I
GPC'06 Proceedings of the First international conference on Advances in Grid and Pervasive Computing
Hi-index | 0.00 |
An effective workload distribution has a prime rule on reducing the total execution time of a parallel application on heterogeneous environments, such as computational grids and heterogeneous clusters. Several methods have been proposed in the literature by many researchers in the last decade. This paper presents two approaches to workload distribution based on analytical models developed to performance prediction of parallel applications, named PEMPIs VRP (Vector of Relative Performances). The workload is distributed based on relative performance ratios, obtained by these models. In this work, we present two schemes, static and dynamic, in a research middleware for a heterogeneous network of computers. In the experimental tests we evaluated and compared them using two MPI applications. The results show that, using the VRP's dynamic strategy, we can reduce the imbalance, among the execution time of the processes, in relation to average time from 25% to near of 5%.