masterThesis
Análise de escalabilidade de uma implementação paralela do simulated annealing acoplado
Fecha
2013-03-25Registro en:
SILVA, Kayo Gonçalves e. Análise de escalabilidade de uma implementação paralela do simulated annealing acoplado. 2013. 68 f. Dissertação (Mestrado em Automação e Sistemas; Engenharia de Computação; Telecomunicações) - Universidade Federal do Rio Grande do Norte, Natal, 2013.
Autor
Silva, Kayo Gonçalves e
Resumen
This paper analyzes the performance of a parallel implementation of Coupled Simulated
Annealing (CSA) for the unconstrained optimization of continuous variables problems. Parallel
processing is an efficient form of information processing with emphasis on exploration of
simultaneous events in the execution of software. It arises primarily due to high computational
performance demands, and the difficulty in increasing the speed of a single processing core.
Despite multicore processors being easily found nowadays, several algorithms are not yet suitable
for running on parallel architectures. The algorithm is characterized by a group of Simulated
Annealing (SA) optimizers working together on refining the solution. Each SA optimizer runs
on a single thread executed by different processors. In the analysis of parallel performance and
scalability, these metrics were investigated: the execution time; the speedup of the algorithm
with respect to increasing the number of processors; and the efficient use of processing elements
with respect to the increasing size of the treated problem. Furthermore, the quality of
the final solution was verified. For the study, this paper proposes a parallel version of CSA
and its equivalent serial version. Both algorithms were analysed on 14 benchmark functions.
For each of these functions, the CSA is evaluated using 2-24 optimizers. The results obtained
are shown and discussed observing the analysis of the metrics. The conclusions of the paper
characterize the CSA as a good parallel algorithm, both in the quality of the solutions and the
parallel scalability and parallel efficiency