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Anomalies in Parallel BranchandBound Algorithms
, 1984
"... We consider the effects of parallelizing branchandbound algorithms by expanding several live nodes simultaneously. It is shown that it is quite possible for a parallel branchandbound algorithm using n 2 processors to take more time than one using n 1 processors even though n 1 < n 2 . Further ..."
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Cited by 66 (3 self)
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We consider the effects of parallelizing branchandbound algorithms by expanding several live nodes simultaneously. It is shown that it is quite possible for a parallel branchandbound algorithm using n 2 processors to take more time than one using n 1 processors even though n 1 < n 2 . Furthermore, it is also possible to achieve speedups that are in excess of the ratio n 2 /n 1 . Experimental results with the 0/1Knapsack and Traveling Salesperson problems are also presented.
A Ga Based Multiple Task Allocation Considering Load
, 2000
"... A Distributed Computing System (DCS) comprising networked heterogeneous processors requires e#cient tasks to processor allocation to achieve minimum turnaround time and highest possible throughput. Task allocation in DCS remains an important and relevant problem attracting the attention of resear ..."
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Cited by 4 (0 self)
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A Distributed Computing System (DCS) comprising networked heterogeneous processors requires e#cient tasks to processor allocation to achieve minimum turnaround time and highest possible throughput. Task allocation in DCS remains an important and relevant problem attracting the attention of researchers in the discipline. A good number of task allocation algorithms have been proposed in the literature [39]. This algorithm considered allocation of the modules of a single task to various processing nodes and aim to minimize the turnaround time of the given task. But they did not consider execution of modules belonging to various di#erent tasks (i.e. multiple tasks). In this work we have considered the number of modules that can be accepted by individual processing nodes along with their memory capacities and arrival of multiple disjoint tasks to the DCS from time to time. In this paper, a method based on genetic algorithm is developed which is memory e#cient and give an optimal solution of the problem. The given simulation results also show significant achievement in this regard.
A Parallel Algorithm for Optimal Task Assignment in Distributed Systems
"... An efficient assignment of tasks to the processors is imperative for achieving a fast job turnaround time in a parallel or distributed enviornment. The assignment problem is well known to be NPcomplete, except in a few special cases. Thus heuristics are used to obtain suboptimal solutions in reason ..."
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An efficient assignment of tasks to the processors is imperative for achieving a fast job turnaround time in a parallel or distributed enviornment. The assignment problem is well known to be NPcomplete, except in a few special cases. Thus heuristics are used to obtain suboptimal solutions in reasonable amount of time. While a plethora of such heuristics have been documented in the literature, in this paper we aim to develop techniques for finding optimal solutions under the most relaxed assumptions. We propose a bestfirst search based parallel algorithm that generates optimal solution for assigning an arbitrary task graph to an arbitrary network of homogeneous or heterogeneous processors. The parallel algorithm running on the Intel Paragon gives optimal assignments for problems of medium to large sizes. We believe our algorithms to be novel in solving an indispensable problem in parallel and distributed computing. Keywords: Bestfirst search, parallel processing, task assignment, mapping, distributed systems.