A multi-staged niched evolutionary approach for allocating parallel tasks with joint optimization of performance, energy, and temperature
This paper presents a multi-stage multi-objective evolutionary approach (MS-MOEA) for allocating parallel computations on multi-core processors by joint optimizing performance (P), energy (E), and temperature (T). Evolutionary techniques have been shown to be effective for solving optimization probl...
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| Published in: | Journal of parallel and distributed computing Vol. 134; pp. 65 - 74 |
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| Language: | English |
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01.12.2019
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| ISSN: | 0743-7315, 1096-0848 |
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| Abstract | This paper presents a multi-stage multi-objective evolutionary approach (MS-MOEA) for allocating parallel computations on multi-core processors by joint optimizing performance (P), energy (E), and temperature (T). Evolutionary techniques have been shown to be effective for solving optimization problems, including our own previous work on solving the PET-optimized scheduling (PETOS) problem. There have long been a great many debates and rivalries between various evolutionary approaches, such as the SPEA or NSGA, with regard to their relative matters. The novelty of the proposed MS-MOEA approach is its amalgamation of the basic evolutionary algorithms that are already shown to be highly effective, thereby creating a niche of these techniques. The niche takes advantages of the strengths of each baseline technique for achieving additional enhancement in the precision of the optimization. We propose six multi-stage hybrids, each designed with either niched fitness assignment strategy, or combining populations from multiple MOEAs, or incorporating the problem knowledge into the conventional technique. The experimental results measure the quality of resulting Pareto fronts and demonstrate that the proposed MS-MOEAs yield better optimization for the PETOS problem in achieving three-objective in parallel task-to-core mapping. |
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| AbstractList | This paper presents a multi-stage multi-objective evolutionary approach (MS-MOEA) for allocating parallel computations on multi-core processors by joint optimizing performance (P), energy (E), and temperature (T). Evolutionary techniques have been shown to be effective for solving optimization problems, including our own previous work on solving the PET-optimized scheduling (PETOS) problem. There have long been a great many debates and rivalries between various evolutionary approaches, such as the SPEA or NSGA, with regard to their relative matters. The novelty of the proposed MS-MOEA approach is its amalgamation of the basic evolutionary algorithms that are already shown to be highly effective, thereby creating a niche of these techniques. The niche takes advantages of the strengths of each baseline technique for achieving additional enhancement in the precision of the optimization. We propose six multi-stage hybrids, each designed with either niched fitness assignment strategy, or combining populations from multiple MOEAs, or incorporating the problem knowledge into the conventional technique. The experimental results measure the quality of resulting Pareto fronts and demonstrate that the proposed MS-MOEAs yield better optimization for the PETOS problem in achieving three-objective in parallel task-to-core mapping. |
| Author | Sheikh, Hafiz Fahad Ahmad, Ishfaq |
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| CitedBy_id | crossref_primary_10_1016_j_suscom_2020_100462 crossref_primary_10_3390_computation8020026 crossref_primary_10_1080_17445302_2025_2481513 |
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| Keywords | Task graphs Evolutionary algorithms Energy-efficient computing Task allocation Static scheduling |
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| SubjectTerms | Energy-efficient computing Evolutionary algorithms Static scheduling Task allocation Task graphs |
| Title | A multi-staged niched evolutionary approach for allocating parallel tasks with joint optimization of performance, energy, and temperature |
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