Parameterized Schemes of Metaheuristics: Basic Ideas and Applications With Genetic Algorithms, Scatter Search, and GRASP

Almeida, Francisco; Giménez, Domingo; López-Espín, José J.; Perez-Perez, M.

doi:10.1109/tsmca.2012.2217322

Cited by 36 publications

(15 citation statements)

References 24 publications

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“…Our previous work shows that these metaheuristics can be widely enriched with combinations of the above to derive multiple hybridations with different results and effects . This time, we consider only those three as basic building blocks and, instead, we move the set of 17 parameters to study its influence on a performance per watt basis.…”

Section: Methodsmentioning

confidence: 99%

Energy‐based tuning of metaheuristics for molecular docking on multi‐GPUs

Pérez-Serrano

Imbernón

Cecilia

et al. 2018

Concurrency and Computation

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Summary Virtual Screening (VS) methods simulate molecular interactions in silico to look for the best chemical compound that interacts with a given molecular target. VS is becoming increasingly popular to accelerate the drug discovery process and constitute hard optimization problems with a huge computational cost. To deal with these two challenges, we have created METADOCK, an application that (1) enables a wide range of metaheuristics through a parametrized schema and (2) promotes the use of a multi‐GPU environment within a heterogeneous cluster. Metaheuristics provide approximate solutions in a reasonable time frame, but, given the stochastic nature of real‐life procedures, the energy budget goes hand in hand with acceleration to validate the proposed solution. This paper evaluates energy trade‐offs and correlations with performance for a set of metaheuristics derived from METADOCK. We establish a solid inference from minimal power to maximal performance in GPUs, and from there, to optimal energy consumption. This way, ideal heuristics can be chosen according not only to best accuracy and performance but also to energy requirements. Our study starts with a preselection of parameterized metaheuristic functions, building blocks where we will find optimal patterns from power criteria while preserving parallelism through a GPU execution. We then establish a methodology to figure out the best instances of the parameterized kernels based on energy patterns obtained, which are analyzed from different viewpoints, ie, performance, average power, and total energy consumed. We also compare the best workload distributions for optimal performance and power efficiency among Pascal and Maxwell GPUs on popular Titan models. Our experimental results demonstrate that the most power efficient GPU can be overloaded in order to reduce the total amount of energy required by as much as 20%, finding unique scenarios where Maxwell does it better in execution time, but with Pascal always ahead in performance per watt, reaching peaks of up to 40%.

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Section: Methodsmentioning

confidence: 99%

Energy‐based tuning of metaheuristics for molecular docking on multi‐GPUs

Pérez-Serrano

Imbernón

Cecilia

et al. 2018

Concurrency and Computation

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“…The effectiveness of some heuristics can be very sensitive to the parameter settings [27]. Often, through systematic trials and subsequent statistical analyses that illustrate the potential quality of outcomes, a satisfactory set of parameters is selected (i.e., the parameters are tuned) for the problem to be solved [28]. Hopefully, the parameters are chosen appropriately, but as they are sensitive to the problem size and type, results can vary from one problem to the next as conditions change.…”

Section: Intelligent and Dynamic Parameterization Of A Search Processmentioning

confidence: 99%

Forest Planning Heuristics—Current Recommendations and Research Opportunities for s-Metaheuristics

Bettinger

Boston

2017

Forests

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Adaptive forest management requires planning and implementation of activities designed to maintain or improve forest conditions, and in support of these endeavors knowledge of silviculture, economics, operations research, and other allied fields are necessary. With regard to forest planning, traditional (exact) mathematical techniques along with heuristics have been demonstrated as useful in developing alternative courses of action for forest managers to consider. In this discussion paper, we present six areas of future work with regard to investigations into the development of heuristics, along with several recommendations that are based on our experiences. These areas include process improvements, reversion strategies, destruction and reconstruction strategies, intelligent or dynamic parameterization approaches, intelligent termination or transitioning approaches, and seeding strategies. We chose the six areas based on our experiences in developing forest planning heuristics. These areas reflect our opinion of where future research might concentrate. All of these areas of work have the potential to enhance the capabilities and effectiveness of heuristic approaches when applied to adaptive forest management problems.

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“…There are several metaheuristic methods for optimization problems . Some are applied to the determination of optimum values of the parameters of parallel algorithms or tasks‐to‐processors assignation problems in heterogeneous systems and could be applied to the optimization problem in hand (obtaining the value of the parameters that give the lowest execution time for each size). Metaheuristics based on populations require many evaluations, which means large installation times.…”

Section: Autotuning Techniquesmentioning

confidence: 99%

Guided installation of basic linear algebra routines in a cluster with manycore components

Cuenca

García

Giménez

et al. 2017

Concurrency and Computation

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Summary Computational systems are nowadays composed of basic computational components that share multiprocessors and coprocessors of different types, typically several graphics processing units (GPUs) or many integrated cores (MICs), and those computational components are combined in heterogeneous clusters of nodes with different characteristics, including coprocessors of different types, with varying numbers of nodes at different speeds. The software previously developed and optimized for simpler system needs to be redesigned and reoptimized for these new, more complex systems. The adaptation to hybrid multicore + multiGPU and multicore + multiMIC of autotuning techniques for basic linear algebra routines is analyzed. The matrix‐matrix multiplication kernel, which is optimized for different computational system components through guided experimentation, is studied. The routine is installed for each node in the cluster, and the information generated from individual installations may be used for a hierarchical installation in a cluster. The basic matrix‐matrix multiplication may, in turn, be used inside higher level routines, which delegate their efficient execution to the optimization of the lower level routine. Experimental results are satisfactory in different multicore + multiGPU and multicore + multiMIC systems. So the guided search of execution configurations for satisfactory execution times proves to be a useful tool for heterogeneous systems, where the complexity of the system means a correct use of highly efficient routines and libraries is difficult.

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Parameterized Schemes of Metaheuristics: Basic Ideas and Applications With Genetic Algorithms, Scatter Search, and GRASP

Cited by 36 publications

References 24 publications

Energy‐based tuning of metaheuristics for molecular docking on multi‐GPUs

Energy‐based tuning of metaheuristics for molecular docking on multi‐GPUs

Forest Planning Heuristics—Current Recommendations and Research Opportunities for s-Metaheuristics

Guided installation of basic linear algebra routines in a cluster with manycore components

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