Thierry Lavoie
ABSTRACT
Graphics Processing Unit (GPU) have been around for a while. Although they are primarily used for high-end 3D graphics processing, their use is now acknowledged for general massive parallel computing. This paper presents an original technique based on [10] to compute many instances of the longest common subsequence problem on a generic GPU architecture using classic DP-matching [7]. Application of this algorithm has been found useful to address the problem of filtering false positives produced by metrics-based clone detection methods. Experimental results of this application are presented along with a discussion of possibilities of using GPUs for other cloning related problems.
- Eclipse. http://www.eclipse.org.Google Scholar
- Tomcat. http://tomcat.apache.org.Google Scholar
- AMD/ATI. Ati ctm guide technical reference manual, version 1.01. http://ati.amd.com, 2006.Google Scholar
- S. Bellon, R. Koschke, G. Antoniol, J. Krinke, and E. Merlo. Comparison and evaluation of clone detection tools. IEEE Transactions on Software Engineering, 33(9):577--591, 2007. Google ScholarDigital Library
- I. Buck and T. Purcell. GPU Gems, chapter 37, pages 621--636. Addison Wesley, 2004.Google Scholar
- S. Che, J. Li, J. Sheaffer, K. Skadron, and J. Lach. Accelerating compute-intensive applications with gpus and fpgas. In Symposium on Applications Specific Processors, 2008, pages 101--107, 2008. Google ScholarDigital Library
- T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein. Introduction to algorithms, chapter 15. MIT Press, second edition, 2001. Google ScholarDigital Library
- K. Group. Opencl-the open standard for parallel programming of heterogeneous systems. http://www.khronos.org/opencl/, 2009.Google Scholar
- P. Kipfer and R. Westermann. GPU Gems 2, chapter 46, pages 733--746. Addison Wesley, 2005.Google Scholar
- S. Manavski and G. Valle. Cuda compatible gpu cards as efficient hardware accelerators for smith-waterman sequence alignment. In BITS: Annual Meeting, Naples, Italy, April 2007.Google Scholar
- D. T. Marr, F. Binns, D. L. Hill, G. Hinton, D. A. Koufaty, J. A. Miller, and M. Upton. Hyper-threading technology. 6, february 2002.Google Scholar
- J. Mayrand, C. Leblanc, and E. Merlo. Experiment on the automatic detection of function clones in a software system using metrics. In Proceedings of the International Conference on Software Maintenance - IEEE Computer Society Press, pages 244--253, Monterey, CA, Nov 1996. Google ScholarDigital Library
- E. Merlo, G. Antoniol, M. D. Penta, and F. Rollo. Linear complexity object-oriented similarity for clone detection and software evolution analysis. In Proceedings of the International Conference on Software Maintenance - IEEE Computer Society Press, pages 412--416. IEEE Computer Society Press, 2004. Google ScholarDigital Library
- E. Merlo and T. Lavoie. Computing structural types of clone syntactic blocks. In Working Conference on Reverse Engineering, pages 274--278. IEEE, 2009. Google ScholarDigital Library
- P. Micikevicius. GPU Gems 2, chapter 43, pages 695--702. Addison Wesley, 2005.Google Scholar
- NVIDIA. Nvidia cuda compute unified device architecture, programming guide version 1.0. http://www.nvidia.com, 2007.Google Scholar
- M. Schatz and C. Trapnell. Fast exact string matching on gpu. http://www.nvidia.com, 2007.Google Scholar
- E. Seamans and T. Alexander. GPU Gems 3, chapter 35, pages 771--783. Addison Wesley, 2008.Google Scholar
Index Terms
- Challenging cloning related problems with GPU-based algorithms
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