Ray S. Chen

Learn More
The majority of auto-tuning research in HPC to date has focused on improving the performance of a single objective function. More recently, interest has grown in optimizing multiple objective functions simultaneously, for example balancing the trade-off between execution time and energy consumption. Evolutionary algorithms for multi-objective optimization(More)
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(More)
The benefit of automated application tuning has been the focus of numerous research projects, yet applying this technology remains a completely manual and labor-intensive process. This paper explores first steps towards reducing the adoption cost of auto-tuning in the context of Chapel; a parallel programming language whose development is being led by Cray(More)
  • 1