Abstract: A method for iteratively autotuning a high-performance computing system that depends on a set of parameters. Performance is first evaluated two or more times with the current values of the parameters. Afterward at least two evaluations, the median performance is evaluated. The median is then tested against a rule based on a filtering threshold. If the median does not the rule, the current values of the parameters are discarded, and the method is restarted with at least one other value generated by an optimization module; otherwise, a resampling method is performed based on the median and on a confidence interval that decreases with the number of steps of the optimization method.

Abstract: A method for iteratively autotuning a high-performance computing system that depends on a set of parameters. Performance is first evaluated two or more times with the current values of the parameters. Afterward at least two evaluations, the median performance is evaluated. The median is then tested against a rule based on a filtering threshold. If the median does not the rule, the current values of the parameters are discarded, and the method is restarted with at least one other value generated by an optimization module; otherwise, a resampling method is performed based on the median and on a confidence interval that decreases with the number of steps of the optimization method.

Abstract: A method for optimizing the run parameters of a software application on an information processing platform, consisting of iteratively optimizing said parameters on each execution of said application, wherein, for each execution of said application (11), a set of said parameters is determined and a run time of said application with said parameters is determined, and an association between said set and said run time is stored in order to create a history (25); and wherein said set is determined by implementing a genetic optimization algorithm comprising a step (21) consisting of selecting two sets of parameters from said history; a step (22) consisting of creating a new set of parameters by recombining said two sets of parameters; and a step (23) of random mutation of said new set of parameters.