No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Statical Scheduling of Flow Graphs Using Neural Networks
Abstract
In the problem of scheduling data flow computers, the optimization of scheduling needs much cost. The cost often increases in non-polynomial order as the number of the processing elements and tasks increases. Many studies have been tried to decrease the order. We have used Hopfield-type neural network model for this problem. We have achieved the reduction of calculation cost in proportion as the number of tasks and the number of processing elements. Our algorithm schedules flow graphs onto the data flow architectures. Our algorithm optimizes communication delays and parallel processing delays only on the critical path of the graph. So the result of our scheduling is more superior to therecent other studies which employs neural network models. Our method is so good at being processed in parallel for it employs neural networ model.
ResearchGate has not been able to resolve any citations for this publication.
This paper describes practical optimization/approximation algorithms for scheduling a set of partially ordered computational tasks with different processing times onto a multiprocessor system so that the schedule length is minimized. Since this problem belongs to the class of “strong” NP hard problems, we must eliminate the possibility of constructing not only pseudopolynomial time optimization algorithms, but also fully polynomial time approximation schemes unless P = NP.
This paper proposes a heuristic algorithm CP/MISF (Critical Path/Most Immediate Successors First) and an optimization/approximation algorithm DF/IHS (Depth First/ Implicit Heuristic Search). DF/IHS is an excellent scheduling method which can reduce markedly the space complexity and average computation time by combining the branch‐and‐bound method with CP/MISF; it allows us to solve very large‐scale problems with a few hundred tasks.
The problem of optimally assigning the modules of a parallel
program over the processors of a multiple-computer system is addressed.
A sum-bottleneck path algorithm is developed that permits the efficient
solution of many variants of this problem under some constraints on the
structure of the partitions. In particular, the following problems are
solved optimally for a single-host, multiple-satellite system:
partitioning multiple chain-structures parallel programs, multiple
arbitrarily structured serial programs, and single-tree structured
parallel programs. In addition, the problem of partitioning
chain-structured parallel programs across chain-connected systems is
solved under certain constraints. All solutions for parallel programs
are equally applicable to pipelined programs