Accelerating text-based plagiarism detection using GPUs

Abstract

Plagiarism is known as unauthorized use of other's contents in writing and ideas in thinking without proper acknowledgment. There are several tools implemented for text-based plagiarism detection using various methods and techniques. However, these tools become inefficient while handling a large number of datasets due to the process of plagiarism detection which comprises a lot of computational tasks and large memory requirements. Therefore, when we deal with a large number of datasets, there should be a way to accelerate the process by applying acceleration techniques to optimize plagiarism detection. In response to this, we have developed a parallel algorithm using Compute Unified Device Architecture (CUDA) and tested it on a Graphics Processing Unit (GPU) platform. An equivalent algorithm is run on the CPU platform as well. From the comparison of the results, the CPU shows better performance when the number and the size of the documents are small. Meantime, GPU is an effective and efficient platform when handling a large number of documents and is high in data size due to the increase in the amount of parallelism. It was found that for our dataset, the performance of the algorithm on the GPU platform is approximately 6x faster than CPU. Thus, introducing GPU based optimization algorithm to plagiarism detection gives a real solution while handling a large number of data for inter-document plagiarism detection.