Hybrid cluster of multicore CPUs and GPUs for accelerating hyperspectral image hierarchical segmentation

Hierarchical image segmentation is a well-known image analysis and clustering method that is used for hyperspectral image analysis. This paper introduces a parallel implementation of hybrid CPU/GPU for the Recursive Hierarchical Segmentation method (RHSEG) algorithm, in which CPU and GPU work cooperatively and seamlessly, combining benefits of both platforms. RHSEG is a method developed by National Aeronautics and Space Administration (NASA) which is more efficient than other traditional methods for high spatial resolution images. The RHSEG algorithm is also implemented on both GPU cluster and hybrid CPU/GPU cluster and the results are compared with the hybrid CPU/GPU implementation. For single hybrid computational node of 8 cores, a speedup of 6x is achieved using both CPU and GPU. On a computer cluster of 16 hybrid CPU/GPU nodes, an average speed up of 112x times is achieved over the sequential CPU implementation.