optimized GPU-based Monte Carlo algorithms'
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Monte Carlo ,GPU ,CUDA ,Potts model ,Metastability

Introduction {#Intro}
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The tremendous advances allowed by the usage of numerical simulations in the last decades have promoted these techniques to the status of indispensable tools in modern Statistical Mechanics research. Notwithstanding, many important theoretical problems in the field still remain difficult to handle due to limitations in the available computational capabilities. Among many others, typical issues that challenge the numerical treatment concern systems with slow dynamics (i.e., dynamical processes that involve very different time scales) and/or strong finite size effect, which require fast simulations of a very large number of particles. Some typical examples we may cite are spin glass transitions [@Fisher-Hertz-book1993], glassy behavior [@Ko2003; @Binder-Kob-book2005] and grain growth [@Cu2010]. In such kind of problems the state of the art is usually launched by novel numerical approaches or extensive computer simulations. In this sense, the advent of massive parallel computing continuously opens new possibilities but, at the same time, creates a demand for new improved algorithms. In particular, the usage of GPU cards (short for Graphics Processing Units) as parallel processing devices is emerging as a powerful tool for numerical simulations in Statistical Mechanics systems 