Efficient computation of motif discovery on Intel Many Integrated Core (MIC) Architecture.
BMC Bioinformatics
; 19(Suppl 9): 282, 2018 Aug 13.
Article
em En
| MEDLINE
| ID: mdl-30367570
ABSTRACT
BACKGROUND:
Novel sequence motifs detection is becoming increasingly essential in computational biology. However, the high computational cost greatly constrains the efficiency of most motif discovery algorithms.RESULTS:
In this paper, we accelerate MEME algorithm targeted on Intel Many Integrated Core (MIC) Architecture and present a parallel implementation of MEME called MIC-MEME base on hybrid CPU/MIC computing framework. Our method focuses on parallelizing the starting point searching method and improving iteration updating strategy of the algorithm. MIC-MEME has achieved significant speedups of 26.6 for ZOOPS model and 30.2 for OOPS model on average for the overall runtime when benchmarked on the experimental platform with two Xeon Phi 3120 coprocessors.CONCLUSIONS:
Furthermore, MIC-MEME has been compared with state-of-arts methods and it shows good scalability with respect to dataset size and the number of MICs. Source code https//github.com/hkwkevin28/MIC-MEME .Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Software
/
Regiões Promotoras Genéticas
/
Biologia Computacional
/
Elementos Reguladores de Transcrição
/
Motivos de Nucleotídeos
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2018
Tipo de documento:
Article