On the assignment problem of arbitrary process systems to heterogeneous distributed computer systems

Bowen, N.S.; Nikolaou, Christos; Ghafoor, Arif

doi:10.1109/12.127439

Cited by 56 publications

(24 citation statements)

References 15 publications

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“…In the past a number of static graph-based mapping algorithms for heterogeneous computing environments have been proposed [3,[18][19][20][21][22][23][24][25][26][27][28][29][30] and evaluated [31], to approximate optimal mapping solution.…”

Section: Related Workmentioning

confidence: 99%

A static mapping heuristics to map parallel applications to heterogeneous computing systems

Baraglia

Ferrini

Ritrovato

2005

Concurrency Computat.: Pract. Exper.

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SUMMARYIn order to minimize the execution time of a parallel application running on a heterogeneously distributed computing system, an appropriate mapping scheme is needed to allocate the application tasks to the processors. The general problem of mapping tasks to machines is a well-known NP-hard problem and several heuristics have been proposed to approximate its optimal solution. In this paper we propose a static graph-based mapping algorithm, called Heterogeneous Multi-phase Mapping (HMM), which permits suboptimal mapping of a parallel application onto a heterogeneous computing distributed system by using a local search technique together with a tabu search meta-heuristic. HMM allocates parallel tasks by exploiting the information embedded in the parallelism forms used to implement an application, and considering an affinity parameter, that identifies which machine in the heterogeneous computing system is most suitable to execute a task. We compare HMM with some leading techniques and with an exhaustive mapping algorithm. We also give an example of mapping of two real applications using HMM. Experimental results show that HMM performs well demonstrating the applicability of our approach.

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Section: Related Workmentioning

confidence: 99%

A static mapping heuristics to map parallel applications to heterogeneous computing systems

Baraglia

Ferrini

Ritrovato

2005

Concurrency Computat.: Pract. Exper.

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“…This type of scheduling problem is better described as the assignment or mapping problem [6]. The assignment can be done in a number of ways using heuristics, graph theoretic models [6], clustering [7], integer programming [36], or by many other optimization techniques, depending upon the nature of the application and the target system. These types of techniques have also been termed as static scheduling techniques [40].…”

Section: Related Work and Motivation For A New Approachmentioning

confidence: 99%

Semi-distributed load balancing for massively parallel multicomputer systems

Ahmad

Ghafoor

1991

IIEEE Trans. Software Eng.

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“…Among the previous works that address Stone's original problem, those that use task clustering (for example [7,15,31,34,36,52]) are of particular interest to us because we are improving upon these works. These works use clustering approaches in which highly interacting tasks are merged to reduce the original problem into a smaller one.…”

Section: Contributionsmentioning

confidence: 99%

“…This type of algorithms are called single-phase heuristics. Some other works [7,15,34,52] obtain task-to-processor assignments in an assignment phase separated from the clustering phase and hence are called two-phase heuristics.…”

Section: Contributionsmentioning

confidence: 99%

Task assignment in heterogeneous computing systems

Uçar

Aykanat

Kaya

et al. 2006

Journal of Parallel and Distributed Computing

118

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The problem of task assignment in heterogeneous computing systems has been studied for many years with many variations. We consider the version in which communicating tasks are to be assigned to heterogeneous processors with identical communication links to minimize the sum of the total execution and communication costs. Our contributions are three fold: a task clustering method which takes the execution times of the tasks into account; two metrics to determine the order in which tasks are assigned to the processors; a refinement heuristic which improves a given assignment. We use these three methods to obtain a family of task assignment algorithms including multilevel ones that apply clustering and refinement heuristics repeatedly. We have implemented eight existing algorithms to test the proposed methods. Our refinement algorithm improves the solutions of the existing algorithms by up to 15% and the proposed algorithms obtain better solutions than these refined solutions.

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On the assignment problem of arbitrary process systems to heterogeneous distributed computer systems

Cited by 56 publications

References 15 publications

A static mapping heuristics to map parallel applications to heterogeneous computing systems

A static mapping heuristics to map parallel applications to heterogeneous computing systems

Semi-distributed load balancing for massively parallel multicomputer systems

Task assignment in heterogeneous computing systems

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