Scheduling precedence graphs in systems with interprocessor communication times
SIAM Journal on Computing
Static scheduling algorithms for allocating directed task graphs to multiprocessors
ACM Computing Surveys (CSUR)
Monitoring parallel programs for performance tuning in cluster environments
Parallel program development for cluster computing
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
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
Euro-Par '02 Proceedings of the 8th International Euro-Par Conference on Parallel Processing
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The efficient mapping of parallel tasks is essential in order to exploit the gain from parallelisation. In this work, we focus on modelling and mapping message-passing applications that are defined by the programmer with an arbitrary interaction pattern among tasks. A new model is proposed, known as TTIG (Temporal Task Interaction Graph), which captures not only computation and communication costs, but also the percentages of concurrency between tasks. From this model, a mapping strategy is developed that minimises expected execution time by properly exploiting task parallelism. The effectiveness of this approach has been proven for a real image-processing application on a cluster of PCs.