Scheduling a divisible task in a two-dimensional toroidal mesh
Proceedings of the third international conference on Graphs and optimization
Asymptotically optimal algorithms for job shop scheduling and packet routing
Journal of Algorithms
Scheduling Divisible Loads in Parallel and Distributed Systems
Scheduling Divisible Loads in Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems
Experiments with Scheduling Divisible Tasks in Clusters of Workstations
Euro-Par '00 Proceedings from the 6th International Euro-Par Conference on Parallel Processing
Optimal sharing of bags of tasks in heterogeneous clusters
Proceedings of the fifteenth annual ACM symposium on Parallel algorithms and architectures
Simgrid: A Toolkit for the Simulation of Application Scheduling
CCGRID '01 Proceedings of the 1st International Symposium on Cluster Computing and the Grid
Sharing Partitionable Workloads in Heterogeneous NOWs: Greedier Is Not Better
CLUSTER '01 Proceedings of the 3rd IEEE International Conference on Cluster Computing
Autonomous Protocols for Bandwidth-Centric Scheduling of Independent-Task Applications
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
Scheduling Strategies for Master-Slave Tasking on Heterogeneous Processor Platforms
IEEE Transactions on Parallel and Distributed Systems
Scheduling divisible loads with return messages on heterogeneous master-worker platforms
HiPC'05 Proceedings of the 12th international conference on High Performance Computing
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This paper presents new techniques for master-slave tasking on tree-shaped networks with fully heterogeneous communication and processing resources. A large number of independent, equal-sized tasks are distributed from the master node to the slave nodes for processing and return of result files. The network links present bandwidth asymmetry, i.e. the send and receive bandwidths of a link may be different. The nodes can overlap computation with at most one send and one receive operation. A centralized algorithm that maximizes the platform throughput under static conditions is presented. Thereafter, we propose several distributed heuristics making scheduling decisions based on information estimated locally. Extensive simulations demonstrate that distributed heuristics are better suited to cope with dynamic environments, but also compete well with centralized heuristics in static environments.