Parallel Computing Using CUDA and MultiThreading in Background Removal Process

Parallel processing involves challenges on data dependency. In this work, we presented an investigation of performance into image background removal using the Rembg algorithms incorporating parallel computing techniques including Multithreading and Compute Unified Device Architecture (CUDA). In the background subtraction algorithm, each pixel of the image is compared with the pixels around it. Thus, it will be difficult to parallelise the system if the pixel is being accessed at the same time; otherwise, it will lead to data corruption or inconsistent results. CUDA and Multithreading are utilised in 2, 4 and 8 threads on two different hardware specification machines. Results show the proposed work achieves a maximum of 5.3092 and 5.7913 speed up value in CUDA and Multithreading respectively. In a nutshell, dynamic thread allocation and advanced load balancing algorithms that adapt to changing workloads in real-time can ensure that the computational workload is distributed evenly among threads and GPU cores.