Name
Abstract | ||
|---|---|---|
With the amount of sequence data deluge as a result of next generation sequencing, there comes the need to leverage the large-scale biological sequence data. Therefore, the role of high performance computational methods for mining interesting information solely from these sequence data becomes increasingly important. Almost every research issue in bioinformatics counts on the inter-relationship between sequences, structure and function. Although pairwise statistical significance (PSS) has been found to be capable of accurately mining related sequences (homologs), its estimation is both computationally and data intensive. To prevent it from being a performance bottleneck, high performance computation (HPC) approaches are used for accelerating the computation. In this chapter, we first present the algorithm of pairwise statistical significance estimation, then highlight the use of such HPC approaches for its acceleration employing multi-core CPU and many-core GPU, both of which enable significant performance improvement for pairwise statistical significance estimation (PSSE). © 2013 The authors and IOS Press. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.3233/978-1-61499-324-7-194 | High Performance Computing Workshop |
Keywords | Field | DocType |
high performance computation (HPC),many-core GPU,multi-core CPU,pairwise statistical significance,sequence mining | Pairwise comparison,CUDA,Computer science,Theoretical computer science,Sequential Pattern Mining | Conference |
Volume | Issue | ISSN |
24 | null | null |
Citations | PageRank | References |
0 | 0.34 | 17 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuhong Zhang | 1 | 0 | 0.34 |
| Feng Chen | 2 | 5 | 4.51 |