Solving longest common subsequence and related problems on graphical processing units
Modern graphical processing units (GPUs) offer much more computational power than modern central processing units. Therefore, it is natural that GPUs are applied not only for their original purposes, but also for general processing (GPGPU). In the field of sequence processing, one of the most important problems is the measuring of sequence similarity. There are many sequence similarity measures, e.g. edit distance, longest common subsequence length, and their derivatives. We examine the possibility of speeding up the algorithms computing some of them. We chose three measures useful in different situations. The experimental results show that the GPU versions of the examined algorithms are faster than their serial counterparts by a factor between 4 and 65. Copyright (C) 2010 John Wiley & Sons, Ltd.
longest common subsequence graphical processing units constrained longest common subsequence transposition invariance general processing using gpu cuda