Comparison of parallel central processing unit‐ and graphics processing unit‐based implementations of greedy string tiling algorithm for source code plagiarism detection
Summary Massive‐enrollment computing courses often involve some practical training through programming assignments and projects that are frequent targets for plagiarism. Source code similarity detection tools are used to prevent such misbehavior. Parallel processing has recently become a viable tech...
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| Vydáno v: | Concurrency and computation Ročník 34; číslo 21 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Hoboken, USA
John Wiley & Sons, Inc
25.09.2022
Wiley Subscription Services, Inc |
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| ISSN: | 1532-0626, 1532-0634 |
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| Abstract | Summary
Massive‐enrollment computing courses often involve some practical training through programming assignments and projects that are frequent targets for plagiarism. Source code similarity detection tools are used to prevent such misbehavior. Parallel processing has recently become a viable technique for speeding up the processing of large workloads. This article examines the parallelization of a source code similarity detection method based on the greedy string tiling and Karp–Rabin algorithms. Both CPU and GPU parallelization approaches are discussed. The CPU implementation uses Pthreads, whereas the GPU implementation employs CUDA. Depending on the evaluated dataset which consists of real student assignment codes, speedups of up to seven times over the sequential version of the code are achieved. Evaluation results on both platforms are compared and discussed in detail. |
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| AbstractList | Massive‐enrollment computing courses often involve some practical training through programming assignments and projects that are frequent targets for plagiarism. Source code similarity detection tools are used to prevent such misbehavior. Parallel processing has recently become a viable technique for speeding up the processing of large workloads. This article examines the parallelization of a source code similarity detection method based on the greedy string tiling and Karp–Rabin algorithms. Both CPU and GPU parallelization approaches are discussed. The CPU implementation uses Pthreads, whereas the GPU implementation employs CUDA. Depending on the evaluated dataset which consists of real student assignment codes, speedups of up to seven times over the sequential version of the code are achieved. Evaluation results on both platforms are compared and discussed in detail. Summary Massive‐enrollment computing courses often involve some practical training through programming assignments and projects that are frequent targets for plagiarism. Source code similarity detection tools are used to prevent such misbehavior. Parallel processing has recently become a viable technique for speeding up the processing of large workloads. This article examines the parallelization of a source code similarity detection method based on the greedy string tiling and Karp–Rabin algorithms. Both CPU and GPU parallelization approaches are discussed. The CPU implementation uses Pthreads, whereas the GPU implementation employs CUDA. Depending on the evaluated dataset which consists of real student assignment codes, speedups of up to seven times over the sequential version of the code are achieved. Evaluation results on both platforms are compared and discussed in detail. |
| Author | Tomašević, Milo V. Mišić, Marko J. |
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Massive‐enrollment computing courses often involve some practical training through programming assignments and projects that are frequent targets for... Massive‐enrollment computing courses often involve some practical training through programming assignments and projects that are frequent targets for... |
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| SubjectTerms | Central processing units CPUs CUDA Evaluation Graphics processing units Greedy algorithms greedy‐string‐tiling Parallel processing parallelization plagiarism detection Similarity Source code source code similarity detection Strings Tiling |
| Title | Comparison of parallel central processing unit‐ and graphics processing unit‐based implementations of greedy string tiling algorithm for source code plagiarism detection |
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