BLVector: Fast BLAST-Like Algorithm for Manycore CPU With Vectorization

Frontiers in Genetics

Gabriel Dorado¹  Sergio Gálvez²  Javier Caselli²  Pilar Hernandez³  Federico Agostini⁴ 

[1] Departamento Bioquímica y Biología Molecular, Campus Rabanales C6-1-E17, Campus de Excelencia Internacional Agroalimentario (ceiA3), Universidad de Córdoba, Córdoba, Spain;Departamento Lenguajes y Ciencias de la Computación, Universidad de Málaga, Málaga, Spain;Instituto de Agricultura Sostenible (IAS-CSIC), Consejo Superior de Investigaciones Científicas, Córdoba, Spain;Instituto de Botánica del Nordeste (IBONE), Universidad Nacional del Nordeste, Corrientes, Argentina;

关键词: proteins;    parallel algorithm;    benchmarking;    pairwise alignment;    database search;   

DOI  :  10.3389/fgene.2021.618659

来源: DOAJ

【 摘 要 】

New High-Performance Computing architectures have been recently developed for commercial central processing unit (CPU). Yet, that has not improved the execution time of widely used bioinformatics applications, like BLAST+. This is due to a lack of optimization between the bases of the existing algorithms and the internals of the hardware that allows taking full advantage of the available CPU cores. To optimize the new architectures, algorithms must be revised and redesigned; usually rewritten from scratch. BLVector adapts the high-level concepts of BLAST+ to the x86 architectures with AVX-512, to harness their capabilities. A deep comprehensive study has been carried out to optimize the approach, with a significant reduction in time execution. BLVector reduces the execution time of BLAST+ when aligning up to mid-size protein sequences (∼750 amino acids). The gain in real scenario cases is 3.2-fold. When applied to longer proteins, BLVector consumes more time than BLAST+, but retrieves a much larger set of results. BLVector and BLAST+ are fine-tuned heuristics. Therefore, the relevant results returned by both are the same, although they behave differently specially when performing alignments with low scores. Hence, they can be considered complementary bioinformatics tools.

【 授权许可】

Unknown