Marine bathymetry processing through GPGPU virtualization in high performance cloud computing

Fast technology development has influenced the widespread use of low-power devices in different scientific, environmental, and everyday life areas, giving birth to the Internet of Things. In this paper, we focus on the context of marine studies, addressing the problem of marine bathymetry data processing and analysis via pervasive and Internet-connected sensors and low-power distributed devices. Pervasive and Internet-connected low-power devices (as the components involved in the sensing and processing actions) made diverse and different "things" as a worldwide-distributed system. Given the high complexity of the algorithms involved in these studies, which usually involve general-purpose graphic processing unit (GPGPU) computation, it is impossible for the limited devices to perform the required calculations. To overcome these limitations, in this paper, we propose and implement a vertical application of GVirtuS, the open-source GPGPU virtualization and remoting service, for achieving high performance geographical data interpolation in a high performance cloud computing scenario. We present an innovative implementation by comparing, in terms of performance and accuracy, the inverse distance weighting and kriging interpolation methods in their parallel implementations leveraging on CUDA-enabled GPGPUs. We present a real-world use case related to high-resolution bathymetry interpolation in a crowdsource data context in the Bay of Pozzuoli, Italy.