Evaluating parallel simulated evolution strategies for VLSI cell placement

Abstract

Abstract

Parallel Strategies for Stochastic Evolution

“…However, due to the nature of data dependencies involved, this strategy is not suited to a coarse grained parallel environment, where node-to-node communications are high. This was confirmed by the results obtained when this strategy was applied to parallelize SimE for the same problem [7].…”

“…The net effect is no noticeable benefit from cooperative parallel searches. It should be noted that similarly poor results were obtained for SimE with this strategy [7].…”

“…In this work, parallel algorithms for StocE are presented, considering a spectrum of parallel models [4]. The approaches are also compared with parallel SimE due to the similarities in both heuristics [7]. VLSI cell placement is used as an optimization problem and the goal is to achieve scalable speedups using a low-cost cluster computing environment.…”

Parallel metaheuristics: recent advances and new trends

“…Here, parallel metaheuristics provide a decisive help to tackle learning problems that handle large volume of data (Bouamama, ), and those control problems that involve complex training procedures (Hereford, , ; Huang et al., ). Bioinformatics, an emergent scientific field where parallel models of metaheuristics are helpful tools to cope with computationally expensive optimization problems in molecular biology that often also need to manage very large amount of data, such as sequence alignment (Gomes et al., ; Zola et al., ), DNA sequencing (Hongwei and Yanhua, ; Wirawan et al., ), gene finding (Rausch et al., ), genome assembly (Alba and Luque, ; Nebro et al., ), drug design (Boisson et al., ), protein structure alignment/prediction (Chu and Zomaya, ; Guo et al., ; Islam and Ngom, ; Tantar et al., ), phylogenetic inference (Blagojevic et al., ; Cancino et al., ; Grouchy et al., ), and other related problems (Guarracino et al., ; Martins et al., ; Nebro et al., ). Engineering design, where systems have many components, a large design space, and they usually involve functions with huge computation demands. These characteristics make parallel metaheuristics one of the most promising alternatives to get accurate solutions in reasonable execution times for complex tasks such as aerodynamic optimization and airfoil design (Asouti and Giannakoglou, ; Lim et al., ), design optimization of turbomachinery blade rows (Sasaki et al., ), electronic circuit and VLSI design (Alba et al., ; Lau et al., ; Sait et al., , ), antenna design (Kalinli et al., ; Weis and Lewis, ), signal processing (Li et al., ), etc. Hydraulic engineering, where parallel metaheuristics have been used to efficiently deal with real‐world scenarios arising in water supply network design optimization (López‐Ibánez, ), groundwater source identification (Babbar and Minsker, ; Mirghania et al., ; Sinha and Minsker, ), and multiobjective groundwater problems (Tang et al., ). Information processing, classification, and data mining, where parallel metaheuristics significantly help with the main challenge in this field, which is related to dealing with huge volumes of data, such as in feature selection and classification (Hamdani et al., ; Lopez et al., ), classification rules discovery (Chen ...…”