Ray S. Chen

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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)
This paper is a quality control system involved in using data mining to discover the main inconsistency reasons in the manufacturing process of semiconductor plants and compare the correctness of classification analysis of the two methods, so as to set up a quality control system providing an efficiency tool for analyzing problems, with a view to(More)
The speed at which microprocessors can perform computations is increasing faster than the speed of access to main memory, making efficient use of memory caches ever more important. Because of this, information about the cache behavior of applications is valuable for performance tuning. To be most useful to a programmer, this information should be presented(More)
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