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.
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)