| dc.description.abstract |
Plagiarism is known as an unauthorized use of
other’s contents in writing and ideas in thinking without proper
acknowledgment. There are several tools implemented for textbased 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 of a lot of computational
tasks and large memory requirement. 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 the plagiarism detection. In response to this, we have
developed a parallel algorithm using Computer Unified Device
Architecture (CUDA) and tested it on a Graphical Processing
Unit (GPU) platform. An equivalent algorithm is run on CPU
platform as well. From the comparison of the results, 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 high in data size due to the increase in the amount of
parallelism. It was found out 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 the plagiarism detection gives
a real solution while handling a large number of data for inter-document plagiarism detection. |
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