Name
Abstract | ||
|---|---|---|
HMMer is a widely used tool for protein sequence homology detection, as well as functional annotation of homologous protein sequences, and protein family classification. The HMMer program is based upon a Viterbi algorithm coded in C, and is quite time consuming. Significant efforts have been undertaken to accelerate this program using custom special purpose hardware, as well as more recent attempts to leverage commodity special purpose hardware. This work will report on several minimally invasive code refactoring efforts independently undertaken by the authors, and their significant performance impact on wall clock execution time of the entire program for various test cases. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/AINA.2006.67 | Advanced Information Networking and Applications, 2006. AINA 2006. 20th International Conference |
Keywords | Field | DocType |
C language,biology computing,hidden Markov models,proteins,sequences,C program,HMMer search,Opteron processor,Viterbi algorithm,custom special purpose hardware,functional annotation,hidden Markov model,invasive recoding,protein sequence homology detection | Annotation,Computer science,Parallel computing,Execution time,Test case,Artificial intelligence,Hidden Markov model,Code refactoring,Viterbi algorithm,Machine learning,Distributed computing | Conference |
Volume | ISSN | ISBN |
2 | 1550-445X | 0-7695-2466-4 |
Citations | PageRank | References |
7 | 0.62 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joseph I. Landman | 1 | 7 | 0.62 |
| Joydeep Ray | 2 | 7 | 0.62 |
| John Paul Walters | 3 | 267 | 20.45 |