Heuristic Algorithms for Task Assignment in Distributed Systems
IEEE Transactions on Computers
IEEE Transactions on Parallel and Distributed Systems
Compile-time estimation of communication costs for data parallel programs
Journal of Parallel and Distributed Computing
Allocating Task Interaction Graphs to Processors in Heterogeneous Networks
IEEE Transactions on Parallel and Distributed Systems
Performance Comparison of Strategies for Static Mapping of Parallel Programs
HPCN Europe '97 Proceedings of the International Conference and Exhibition on High-Performance Computing and Networking
Compile-Time Estimation of Communication Costs on Multicomputers
IPPS '92 Proceedings of the 6th International Parallel Processing Symposium
Clustering and Reassignment-Based Mapping Strategy for Message-Passing Architectures
IPPS '98 Proceedings of the 12th. International Parallel Processing Symposium on International Parallel Processing Symposium
Practical Multiprocessor Scheduling Algorithms for Efficient Parallel Processing
IEEE Transactions on Computers
Exploiting Knowledge of Temporal Behaviour in Parallel Programs for Improving Distributed Mapping
Euro-Par '00 Proceedings from the 6th International Euro-Par Conference on Parallel Processing
Proceedings of the 8th European PVM/MPI Users' Group Meeting on Recent Advances in Parallel Virtual Machine and Message Passing Interface
μπ: a scalable and transparent system for simulating MPI programs
Proceedings of the 3rd International ICST Conference on Simulation Tools and Techniques
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An efficient mapping of a parallel program in the processors is vital for achieving a high performance on a parallel computer. When the structure of the parallel program in terms of its task execution times, task dependencies, and amount communication data, is known a priori, mapping can be accomplished statically at compile time. Mapping algorithms start from a parallel application model and map automatically tasks to processors in order to minimise the execution time of the program. In this paper we discuss the current models used in mapping parallel programs: Task Precedence Graph (TPG), Task Interaction Graph (TIG) and we define a new model called Temporal Task Interaction Graph (TTIG). The contribution of the TTIG is that it enhances these two previous models with the ability to explicitly capture the potential degree of parallel execution between adjacent tasks allowing the development of efficient mapping algorithms. Experimentation had been performed in order to show the effectiveness of TTIG model for a set of graphs. The results are compared with the optimal assignment and the obtained using TIG model and they confirm that using the TTIG model, better assignments can be obtained.