A genetic algorithm for scheduling tasks onto dynamically reconfigurable hardware

Yang Qu; Juha-Pekka Soininen; Jari Nurmi

doi:10.1109/ISCAS.2007.378246

A genetic algorithm for scheduling tasks onto dynamically reconfigurable hardware

Yang Qu, Juha-Pekka Soininen, Jari Nurmi

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

10 Citations (Scopus)

Abstract

In this paper, a genetic algorithm (GA) for scheduling tasks onto dynamically reconfigurable devices is presented. The scheduling problem is NP-hard and more complicated than multiprocessor scheduling, because both the task allocation and the configurations need to be carefully managed. The approach has been validated with a number of random task graphs. The results show that the GA approach has good convergence and it is in average 8.6% better than a list-based scheduler for large task graphs of various sizes.

Original language	English
Title of host publication	IEEE International Symposium on Circuits and Systems, ISCAS 2007
Publisher	IEEE Institute of Electrical and Electronic Engineers
Pages	161-164
ISBN (Electronic)	978-1-4244-0921-1
ISBN (Print)	978-1-4244-0920-4
DOIs	https://doi.org/10.1109/ISCAS.2007.378246
Publication status	Published - 2007
MoE publication type	A4 Article in a conference publication
Event	IEEE International Symposium on Circuits and Systems, ISCAS 2007 - New Orleans, LA, United States Duration: 27 May 2007 → 30 May 2007

Conference

Conference	IEEE International Symposium on Circuits and Systems, ISCAS 2007
Abbreviated title	ISCAS 2007
Country/Territory	United States
City	New Orleans, LA
Period	27/05/07 → 30/05/07

Keywords

run-time reconfigurable hardware, genetic algorithm
task scheduling

Access to Document

10.1109/ISCAS.2007.378246

Cite this

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title = "A genetic algorithm for scheduling tasks onto dynamically reconfigurable hardware",

abstract = "In this paper, a genetic algorithm (GA) for scheduling tasks onto dynamically reconfigurable devices is presented. The scheduling problem is NP-hard and more complicated than multiprocessor scheduling, because both the task allocation and the configurations need to be carefully managed. The approach has been validated with a number of random task graphs. The results show that the GA approach has good convergence and it is in average 8.6\% better than a list-based scheduler for large task graphs of various sizes.",

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language = "English",

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booktitle = "IEEE International Symposium on Circuits and Systems, ISCAS 2007",

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note = "IEEE International Symposium on Circuits and Systems, ISCAS 2007, ISCAS 2007 ; Conference date: 27-05-2007 Through 30-05-2007",

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Qu, Y, Soininen, J-P & Nurmi, J 2007, A genetic algorithm for scheduling tasks onto dynamically reconfigurable hardware. in IEEE International Symposium on Circuits and Systems, ISCAS 2007. IEEE Institute of Electrical and Electronic Engineers, pp. 161-164, IEEE International Symposium on Circuits and Systems, ISCAS 2007, New Orleans, LA, United States, 27/05/07. https://doi.org/10.1109/ISCAS.2007.378246

A genetic algorithm for scheduling tasks onto dynamically reconfigurable hardware. / Qu, Yang; Soininen, Juha-Pekka; Nurmi, Jari.
IEEE International Symposium on Circuits and Systems, ISCAS 2007. IEEE Institute of Electrical and Electronic Engineers, 2007. p. 161-164.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - A genetic algorithm for scheduling tasks onto dynamically reconfigurable hardware

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AU - Soininen, Juha-Pekka

AU - Nurmi, Jari

PY - 2007

Y1 - 2007

N2 - In this paper, a genetic algorithm (GA) for scheduling tasks onto dynamically reconfigurable devices is presented. The scheduling problem is NP-hard and more complicated than multiprocessor scheduling, because both the task allocation and the configurations need to be carefully managed. The approach has been validated with a number of random task graphs. The results show that the GA approach has good convergence and it is in average 8.6% better than a list-based scheduler for large task graphs of various sizes.

AB - In this paper, a genetic algorithm (GA) for scheduling tasks onto dynamically reconfigurable devices is presented. The scheduling problem is NP-hard and more complicated than multiprocessor scheduling, because both the task allocation and the configurations need to be carefully managed. The approach has been validated with a number of random task graphs. The results show that the GA approach has good convergence and it is in average 8.6% better than a list-based scheduler for large task graphs of various sizes.

KW - run-time reconfigurable hardware, genetic algorithm

KW - task scheduling

U2 - 10.1109/ISCAS.2007.378246

DO - 10.1109/ISCAS.2007.378246

M3 - Conference article in proceedings

SN - 978-1-4244-0920-4

SP - 161

EP - 164

BT - IEEE International Symposium on Circuits and Systems, ISCAS 2007

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T2 - IEEE International Symposium on Circuits and Systems, ISCAS 2007

Y2 - 27 May 2007 through 30 May 2007

ER -

A genetic algorithm for scheduling tasks onto dynamically reconfigurable hardware

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