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GPUs have been widely used in the past decade to speed up the execution of general purpose applications with high level of parallelism. The efficiency of running general purpose applications on GPUs depends on how well the processing and memory demands of the application is balanced with the hardware resources available on the target GPU and it can significantly affect the power and performance of...
Obtainable power and performance for GPGPU applications on a GPU depend on many architectural and software parameters. Therefore, it is crucial to have a model to explore the design space and highlight a smaller subset of configurations that meet a given system goal. In this study, we present an application specific, MultiObjective Optimizer that explores the design space of GPUs and finds close to...
Different applications have different memory and computational demands. Therefore, obtainable performance and energy efficiency on a GPU depends on how well the GPU resources and application demands are balanced. In this study, we are presenting a Neural Network based predictor to model power and performance of GPGPU applications. The proposed model accurately predicts power and performance for most...
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