Mapping Streaming Applications on Commodity Multi-CPU and GPU On-Chip Processors

In this paper, we consider the problem of efficiently executing streaming applications on commodity processors composed of several cores and an on-chip GPU. Streaming applications, such as those in vision and video analytic, consist of a pipeline of stages and are good candidates to take advantage of this type of platforms. We also consider that characteristics of the input may change while the application is running. Therefore, we propose a framework that adaptively finds the optimal mapping of the pipeline stages. The core of the framework is an analytical model coupled with information collected at runtime used to dynamically map each pipeline stage to the most efficient device, taking into consideration both performance and energy. Our experimental results show that for the evaluated applications running on two different architectures, our model always predicts the best configuration among the evaluated alternatives, and significantly reduces the amount of information that needs to be collected at runtime. This best configuration has, on the average, 20 percent higher throughput than the configuration recommended by a baseline state of the art approach, while the ratio throughput/energy is 43 percent higher. We have measured improvements in throughput and throughput/energy of up-to 81 and 204 percent, respectively, when the model is used to adapt to a video that changes from low to high definition.