Real-time parallel image processing applications on multicore CPUs with OpenMP and GPGPU with CUDA

This paper presents real-time image processing applications using multicore and multiprocessing technologies. To this end, parallel image segmentation was performed on many images covering the entire surface of the same metallic and cylindrical moving objects. Experimental results on multicore CPU with OpenMP platform showed that by increasing the chunk size, the execution time decreases approximately four times in comparison with serial computing. The same experiments were implemented on GPGPU using four techniques: (1) Single image transmission with single pixel processing; (2) Single image transmission with multiple pixel processing; (3) Multiple image transmission with single pixel processing; and (4) Multiple image transmission with multiple pixel processing. All techniques were implemented on GeForce, Tesla K20 and Tesla K40. Experimental results of GPU with CUDA platform showed that by increasing the core number speedup is increased. Tesla K40 gave the best results of 35 and 12 (for the first technique), 36 and 13 (for the second technique), 54 and 16 (for the third technique), 71 and 17 (for the fourth technique) times improvement without and with data transmission time in comparison with serial computing. As a result, users are suggested to use Tesla K40 GPU and Multiple image transmission with multiple pixel processing to get the maximum performance.