Accelerating Big Data processing chain in Image Information Mining using a hybrid HPC approach
The recent development in sensor technology shows the unprecedented growth of Remote Sensing (RS) data archives-Big Data. However, this growth in RS archives has resulted in many processing challenges. The three V's of big data- Volume, Velocity and Variety is highly relevant in situations such as flood, earthquake disaster, where real/near real time processing of data from different RS data sources is vital to deploy rescue operations. In this work, we have demonstrated a high-performance analytics approach- Message Passing Interface (MPI) along with the emerging Graphics Processing Units (GPUs) (i.e. hybrid MPI+GPU) technology to overcome the big data processing limitation. The different processing/analysis stages of our Spatial Image Information Mining (SIIM) system are parallelized using the above approach. The experimental results for parallel segmentation process show the applicability of MPI+GPU hybrid approach in disaster scenario.