Towards fully automatic auto-tuning: Leveraging language features of Chapel

Application auto-tuning has produced excellent results in a wide range of computing domains. Yet adapting an application to use this technology remains a predominately manual and labor intensive process. This paper explores first steps towards reducing adoption cost by focusing on two tasks: parameter identification and range selection. We show how these traditionally manual tasks can be automated in the context of Chapel, a parallel programming language developed by Cray Inc. Potential auto-tuning parameters may be inferred from existing Chapel applications by leveraging features unique to this language. After verification, these parameters may then be passed to an auto-tuner for an automatic search of the induced parameter space. To further automate adoption, we also present Tuna: an auto-tuning shell designed to tune applications by manipulating their command-line arguments. Finally, we demonstrate the immediate utility of this system by tuning two Chapel applications with little or no internal knowledge of the program source.