Applying GPUs for Smith-Waterman Sequence Alignment Acceleration

The Smith-Waterman algorithm is a common localsequence alignment method which gives a high accuracy.However, it needs a high capacity of computation and a largeamount of storage memory, so implementations based oncommon computing systems are impractical. Here, we presentour implementation of the Smith-Waterman algorithm on acluster including graphics cards (GPU cluster) –swGPUCluster. The algorithm implementation is tested on acluster of two nodes: a node is equipped with two dual graphicscards NVIDIA GeForce GTX 295, the other node includes adual graphics cards NVIDIA GeForce 295 and a Tesla C1060card. Depending on the length of query sequences, theswGPUCluster performance increases from 37.33 GCUPS to46.71 GCUPS. This result demonstrates the great computingpower of GPUs and their high applicability in thebioinformatics field

Student perceptions of learning digital literacy online in a leadership program

This chapter presents a study that examined student perceptions of taking a digital literacy class online and its effects on the development of leadership skills in relation to the use of technology. It was found that, in general, the participants tended to be satisfied with this online class. Their perceptions of different types of interactions were discussed. The participants tended to perceive that this class was effective in developing their knowledge and skills in using technology to enact leadership practice. The results have implications in online teaching and learning, group projects and technology learning in leadership development