Accelerating applications using gpus on embedded systems and mobile devices

Abstract—Graphics processing units (GPUs) are capable of achieving remarkable performance improvements for a broad range of applications. However, they have not been widely adopted in embedded systems and mobile devices as accelerators mainly due to their relatively higher power consumption com-pared with embedded microprocessors. In this work, we conduct a comprehensive analysis regarding the feasibility and potential of accelerating applications using GPUs in low-power domains. We use two different categories of benchmarks: (1) the Level 3 BLAS subroutines, and (2) the computer vision algorithms, i.e., mean shift image segmentation and scale-invariant feature transform (SIFT). We carried out our experiments on the Nvidia CARMA development kit, which consists of a Nvidia Tegra 3 quad-core CPU and a Nvidia Quadro 1000M GPU. It is found that the GPU can deliver a remarkable performance speedup compared with the CPU while using a significantly less energy for most benchmarks. Further we propose a hybrid approach to developing applications on platform with GPU accelerators. This approach optimally distributes workload between the parallel GPU and the sequential CPU to achieve the best performance while using the least energy. I