Abstract
OpenCL has emerged as the open standard for parallel programming for heterogeneous platforms enabling a uniform framework to discover, program, and distribute parallel workloads to the diverse set of compute units in the hardware. For that reason, there have been efforts exploring the advantages of parallelism from the OpenCL framework by offloading GPGPU workloads within an HPC cluster environment. In this paper, we present an OpenCL-based remote offloading framework designed for mobile platforms by shifting the motivation and advantages of using the OpenCL framework for the HPC cluster environment into mobile cloud computing where OpenCL workloads can be exported from a mobile node to the cloud. Furthermore, our offloading framework handles service discovery, access control, and data privacy by building the framework on top of a social peer-to-peer virtual private network, Social VPN. We developed a prototype implementation and deployed it into local- and wide-area environments to evaluate the performance improvement and energy implications of the proposed offloading framework. Our results show that, depending on the complexity of the workload and the amount of data transfer, the proposed architecture can achieve more energy efficient performance by offloading than executing locally.