Scheduling precedence graphs in systems with interprocessor communication times
SIAM Journal on Computing
Scheduling parallel program tasks onto arbitrary target machines
Journal of Parallel and Distributed Computing - Special issue: software tools for parallel programming and visualization
Towards an architecture-independent analysis of parallel algorithms
SIAM Journal on Computing
Information Processing Letters
PYRROS: static task scheduling and code generation for message passing multiprocessors
ICS '92 Proceedings of the 6th international conference on Supercomputing
Task Clustering and Scheduling for Distributed Memory Parallel Architectures
IEEE Transactions on Parallel and Distributed Systems
Journal of Parallel and Distributed Computing - Special issue on parallel evolutionary computing
On Exploiting Task Duplication in Parallel Program Scheduling
IEEE Transactions on Parallel and Distributed Systems
A comparison of list schedules for parallel processing systems
Communications of the ACM
Partitioning and Scheduling Parallel Programs for Multiprocessors
Partitioning and Scheduling Parallel Programs for Multiprocessors
Parallel Algorithms and Architectures
Parallel Algorithms and Architectures
Parallel Computer Architecture: A Hardware/Software Approach
Parallel Computer Architecture: A Hardware/Software Approach
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Link contention-constrained scheduling and mapping of tasks
Cluster Computing
Grain Size Determination for Parallel Processing
IEEE Software
Hypertool: A Programming Aid for Message-Passing Systems
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems
DSC: Scheduling Parallel Tasks on an Unbounded Number of Processors
IEEE Transactions on Parallel and Distributed Systems
A Comparison of Heuristics for Scheduling DAGs on Multiprocessors
Proceedings of the 8th International Symposium on Parallel Processing
A Performance Evaluation of CP List Scheduling Heuristics for Communication Intensive Task Graphs
IPPS '98 Proceedings of the 12th. International Parallel Processing Symposium on International Parallel Processing Symposium
Benchmarking the Task Graph Scheduling Algorithms
IPPS '98 Proceedings of the 12th. International Parallel Processing Symposium on International Parallel Processing Symposium
Comparison of Contention Aware List Scheduling Heuristics for Cluster Computing
ICPPW '01 Proceedings of the 2001 International Conference on Parallel Processing Workshops
Graphs and Hypergraphs
Communication Contention in Task Scheduling
IEEE Transactions on Parallel and Distributed Systems
Toward a Realistic Task Scheduling Model
IEEE Transactions on Parallel and Distributed Systems
A New Task Graph Model for Mapping Message Passing Applications
IEEE Transactions on Parallel and Distributed Systems
Contention-aware scheduling with task duplication
Journal of Parallel and Distributed Computing
Advanced reservation-based scheduling of task graphs on clusters
HiPC'06 Proceedings of the 13th international conference on High Performance Computing
Computers & Mathematics with Applications
Task graph pre-scheduling, using Nash equilibrium in game theory
The Journal of Supercomputing
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Many heuristics based on the directed acyclic graph (DAG) have been proposed for the static scheduling problem. Most of these algorithms apply a simple model of the target system that assumes fully connected processors, a dedicated communication sub-system and no contention for the communication resources. Only a few algorithms consider the network topology and the contention for the communication resources. This article evaluates the accuracy of task scheduling algorithms and thus the appropriateness of the applied models. An evaluation methodology is proposed and applied to a representative set of scheduling algorithms. The obtained results show a significant inaccuracy of the produced schedules. Analyzing these results is important for the development of more appropriate models and more accurate scheduling algorithms.