VERTICAL MIGRATION: AN EXPERIMENTAL STUDY OF THE CANDIDATE-SELECTION PROBLEM.

E. Luque; A. Ripoll; T. Diez

doi:10.1049/ip-e.1987.0033

VERTICAL MIGRATION: AN EXPERIMENTAL STUDY OF THE CANDIDATE-SELECTION PROBLEM.

E. Luque, A. Ripoll, T. Diez

Research output: Contribution to journal › Article › Research › peer-review

Abstract

Vertical migration is a technique to improve the performance of a computer system in which the selected primitives (instruction sequences or functions) are moved to a lower level in the software/firmware hierarchy. Concerning the steps to be performed to apply this technique, we have taken into account the selection problem of vertical-migration candidates. To solve the selection of these sequences which leads to the highest time saving for control-store filling, a suboptimal algorithm which considers the structural aspects (interrelationships) between the candidate sequences is presented. To validate this algorithm, it has been run over a selected high number of different sets of candidate sequences, and the results obtained show that the average deviation between the suboptimal solution and the optimal one is under 5%, and that the computer time and mmemory space required to evaluate it are drastically reduced.

Original language	English
Pages (from-to)	177-188
Journal	IEE Proceedings E: Computers and Digital Techniques
Volume	134
Issue number	4
DOIs	https://doi.org/10.1049/ip-e.1987.0033
Publication status	Published - 1 Jan 1987

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title = "VERTICAL MIGRATION: AN EXPERIMENTAL STUDY OF THE CANDIDATE-SELECTION PROBLEM.",

abstract = "Vertical migration is a technique to improve the performance of a computer system in which the selected primitives (instruction sequences or functions) are moved to a lower level in the software/firmware hierarchy. Concerning the steps to be performed to apply this technique, we have taken into account the selection problem of vertical-migration candidates. To solve the selection of these sequences which leads to the highest time saving for control-store filling, a suboptimal algorithm which considers the structural aspects (interrelationships) between the candidate sequences is presented. To validate this algorithm, it has been run over a selected high number of different sets of candidate sequences, and the results obtained show that the average deviation between the suboptimal solution and the optimal one is under 5%, and that the computer time and mmemory space required to evaluate it are drastically reduced.",

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AU - Ripoll, A.

AU - Diez, T.

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N2 - Vertical migration is a technique to improve the performance of a computer system in which the selected primitives (instruction sequences or functions) are moved to a lower level in the software/firmware hierarchy. Concerning the steps to be performed to apply this technique, we have taken into account the selection problem of vertical-migration candidates. To solve the selection of these sequences which leads to the highest time saving for control-store filling, a suboptimal algorithm which considers the structural aspects (interrelationships) between the candidate sequences is presented. To validate this algorithm, it has been run over a selected high number of different sets of candidate sequences, and the results obtained show that the average deviation between the suboptimal solution and the optimal one is under 5%, and that the computer time and mmemory space required to evaluate it are drastically reduced.

AB - Vertical migration is a technique to improve the performance of a computer system in which the selected primitives (instruction sequences or functions) are moved to a lower level in the software/firmware hierarchy. Concerning the steps to be performed to apply this technique, we have taken into account the selection problem of vertical-migration candidates. To solve the selection of these sequences which leads to the highest time saving for control-store filling, a suboptimal algorithm which considers the structural aspects (interrelationships) between the candidate sequences is presented. To validate this algorithm, it has been run over a selected high number of different sets of candidate sequences, and the results obtained show that the average deviation between the suboptimal solution and the optimal one is under 5%, and that the computer time and mmemory space required to evaluate it are drastically reduced.

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