Efficient graphical-processor-unit parallelization algorithm for computing Eigen values

Abstract

Several leading-edge applications such as pathology detection, biometric identification and face recognition are mainly based on blob and line detection. To address this problem, the Eigen value computing has been commonly employed due to its accuracy and robustness. However, the Eigen value computing requires a raised computational processing, an intensive memory data access and a data overlapping which involve higher execution times.To overcome these limitations, we propose in this paper a new parallel strategy to implement the Eigen value computing using a GPU. Our contributions are: (1) to optimize instruction scheduling in order to reduce the computation time, (2) to efficiently partition processing into blocks in order to increase the occupancy of streaming multiprocessors, (3) to provide efficient input data splitting on shared memory to take benefit from its lower access time, ( 4) and to propose new data management of shared memory so as to avoid access memory conflict and reduce memory bank accesses. Experimental results show that our proposed GPU parallel strategy for Eigen value computing achieves speedups of 27 compared to a multithreaded implementation, of 16 compared to a predefined function in the OpenCV library, and of 8 compared to a predefined function in the Cublas library, which are performed into a quad core multi-CPU platform. Next, our parallel strategy is evaluated through an Eigen value based method for retinal thick vessel segmentation which is essential for detecting ocular pathologies. The Eigen value computing is executed in 0.017 seconds, when using STARE database images. Accordingly, we have achieved real-time thick retinal vessel segmentation where average execution time is about 0.039 seconds.