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
IEEE Transactions on Software Engineering
Journal of Parallel and Distributed Computing
Benchmarking and comparison of the task graph scheduling algorithms
Journal of Parallel and Distributed Computing
Static scheduling algorithms for allocating directed task graphs to multiprocessors
ACM Computing Surveys (CSUR)
The Design and Analysis of Computer Algorithms
The Design and Analysis of Computer Algorithms
Bounds on the Number of Processors and Time for Multiprocessor Optimal Schedules
IEEE Transactions on Computers
NP-complete scheduling problems
Journal of Computer and System Sciences
Paper: Assigning dependency graphs onto processor networks
Parallel Computing
Hi-index | 0.00 |
In this paper, we propose new lower bounds on minimum number of processors and minimum time to execute a given program on a multicomputer system, where the program is represented by a directed acyclic task graph having arbitrary execution time and arbitrary communication delays. Additionally, we propose an O(n2+mlog n) time algorithm to compute these bounds for a task graph with n nodes and m arcs. The key ideas of our approach include: (i) identification of certain points called event points and provingthat the intervals havingev ent points as both ends are enough to compute the desired bounds; and (ii) the use of a sweeping technique. Our bounds are shown to be as sharp as the current best known bounds due to Jain and Rajaraman [7]. However, their approach requires O(n2+mlog n+nWerl2) time, where Werl is the earliest execution time of the task graph when arbitrary number of processors are available. Thus, in general, our algorithm performs as good as their algorithm, and exhibits better time complexity for task graphs having Werl O(驴n).