Graph partitioning models for parallel computing
Parallel Computing - Special issue on graph partioning and parallel computing
Multilevel algorithms for multi-constraint graph partitioning
SC '98 Proceedings of the 1998 ACM/IEEE conference on Supercomputing
Journal of Parallel and Distributed Computing
Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing
IEEE Transactions on Parallel and Distributed Systems
A taxonomy of scheduling in general-purpose distributed computing systems
IEEE Transactions on Software Engineering
Parallel Multilevel Algorithms for Multi-constraint Graph Partitioning (Distinguished Paper)
Euro-Par '00 Proceedings from the 6th International Euro-Par Conference on Parallel Processing
Graph Partitioning for Parallel Applications in Heterogeneous Grid Environments
IPDPS '02 Proceedings of the 16th International Symposium on Parallel and Distributed Processing
Graph partitioning for high-performance scientific simulations
Sourcebook of parallel computing
A taxonomy of scientific workflow systems for grid computing
ACM SIGMOD Record
Planning spatial workflows to optimize grid performance
Proceedings of the 2006 ACM symposium on Applied computing
Two-Phase Computation and Data Scheduling Algorithms for Workflows in the Grid
ICPP '07 Proceedings of the 2007 International Conference on Parallel Processing
Performance analysis of dynamic workflow scheduling in multicluster grids
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
Design and Implementation of GXP Make -- A Workflow System Based on Make
ESCIENCE '10 Proceedings of the 2010 IEEE Sixth International Conference on e-Science
| Hi-index | 0.00 |
Among scheduling algorithms of scientific workflows, the graph partitioning is a technique to minimize data transfer between nodes or clusters. However, when the graph partitioning is simply applied to a complex workflow DAG, tasks in each parallel phase are not always evenly assigned to computation nodes since the graph partitioning algorithm is not aware of edge directions that represent task dependencies. Thus, we propose a new method of task assignment based on Multi-Constraint Graph Partitioning. This method relates the dimension of weight vectors to the rank of a task phase defined by traversing the task graph. Our algorithm is implemented in the Pwrake workflow system and evaluated the performance of the Montage workflow using a computer cluster. The result shows that the file size accessed from remote nodes is reduced from 88% to 14% of the total file size accessed during the workflow and that the elapsed time is reduced by 31%.