DNA sequences alignment in multi-GPUs: acceleration and energy payoff

• Published in: BMC bioinformatics Vol. 19; no. Suppl 14; p. 421
• Main Authors: Pérez-Serrano, Jesús, Sandes, Edans, Magalhaes Alves de Melo, Alba Cristina, Ujaldón, Manuel
• Format: Journal Article
• Language: English
• Published: England BioMed Central 20-11-2018; BMC
• Subjects: Acceleration; Algorithms; Alignment; Amino acids; Animals; Base Sequence; Bioinformatics; Computer Graphics; Cost control; CUDA; Deoxyribonucleic acid; DNA; DNA sequences alignment; Electric Power Supplies; Electronic devices; Energy; Energy conservation; Energy consumption; Energy costs; Energy usage; Gene sequencing; Genomes; GPGPU; HPC; Humans; Monkeys & apes; Nucleotide sequence; Pan troglodytes - genetics; Power; Power efficiency; Proteins; Researchers; Sequence Alignment; Supercomputers; Time Factors; CUDA; HPC; Power efficiency; GPGPU; DNA sequences alignment
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-018-2389-6

We present a performance per watt analysis of CUDAlign 4.0, a parallel strategy to obtain the optimal pairwise alignment of huge DNA sequences in multi-GPU platforms using the exact Smith-Waterman method.
Our study includes acceleration factors, performance, scalability, power efficiency and energy costs. We also quantify the influence of the contents of the compared sequences, identify potential scenarios for energy savings on speculative executions, and calculate performance and energy usage differences among distinct GPU generations and models. For a sequence alignment on chromosome-wide scale (around 2 Petacells), we are able to reduce execution times from 9.5 h on a Kepler GPU to just 2.5 h on a Pascal counterpart, with energy costs cut by 60%.
We find GPUs to be an order of magnitude ahead in performance per watt compared to Xeon Phis. Finally, versus typical low-power devices like FPGAs, GPUs keep similar GFLOPS/w ratios in 2017 on a five times faster execution.