Given a parallel program represented by a task graph, the objective of a scheduling algorithm is to minimize the overall execution time of the program by properly assigning the nodes of the graph to the processors. This multiprocessor scheduling problem is NP-complete even with simplifying assumptio
Parallelism measures of task graphs for multiprocessors
โ Scribed by Kamal Kumar Jain; V. Rajaraman
- Publisher
- Elsevier Science
- Year
- 1994
- Weight
- 682 KB
- Volume
- 40
- Category
- Article
- ISSN
- 0165-6074
No coin nor oath required. For personal study only.
๐ SIMILAR VOLUMES
The random graph model of parallel computation introduced by Gelenbe et al. depends on three parameters: n, the number of tasks (vertices); F, the common distribution of T i , . . . , T,, the task processing times, and p = p,, the probability for a given i < j that task i must be completed before ta
A 'standard task graph set' is proposed for fair evaluation of multiprocessor scheduling algorithms. Developers of multiprocessor scheduling algorithms usually evaluate them using randomly generated task graphs. This makes it di cult to compare the performance of algorithms developed in di erent res
We consider scheduling of tasks of parallel programs on multiprocessor systems where tasks have precedence relations and synchronization points. The task graph structures are random variables in the sense that successors to a task do not become known until the task is executed. Thus, as is often the