Scalable computing with parallel tasks
Proceedings of the 2nd Workshop on Many-Task Computing on Grids and Supercomputers
Efficiently migrating real-time systems to multi-cores
ETFA'09 Proceedings of the 14th IEEE international conference on Emerging technologies & factory automation
A systematic approach to classify design-time global scheduling techniques
ACM Computing Surveys (CSUR)
Combined scheduling and mapping for scalable computing with parallel tasks
Scientific Programming - Biological Knowledge Discovery and Data Mining
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After a discussion of the task allocation problem in multi-core processor based parallel system, this paper gives the task allocation model, and proposes an iteration-based heuristic algorithm, which is composed of two rounds of operations, in which the processes are assigned to processing nodes in the first round and threads in process are assigned to processor cores in the second round respectively. Each round of operation partitions the Task Interaction Graph by iterations with backtracking. Evaluation result shows that the algorithm can find near-optimal solutions in reasonable time, and behaves better than genetic algorithm when the number of threads increases, since it can find solutions in much less time than genetic algorithm.