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

Autor: Edans Flavius de Oliveira Sandes, Manuel Ujaldón, Jesús Pérez-Serrano, Alba Cristina Magalhaes Alves de Melo
Rok vydání: 2018
Předmět:
0301 basic medicine
Time Factors
Pan troglodytes
Computer science
Acceleration
CUDA
02 engineering and technology
Parallel computing
DNA sequences alignment
lcsh:Computer applications to medicine. Medical informatics
Biochemistry
03 medical and health sciences
Electric Power Supplies
Structural Biology
Computer Graphics
0202 electrical engineering
electronic engineering
information engineering
Animals
Humans
Power efficiency
lcsh:QH301-705.5
Molecular Biology
Performance per watt
computer.programming_language
020203 distributed computing
Base Sequence
Xeon
Research
Applied Mathematics
GPGPU
Pascal (programming language)
FLOPS
Computer Science Applications
030104 developmental biology
lcsh:Biology (General)
HPC
Scalability
lcsh:R858-859.7
General-purpose computing on graphics processing units
Sequence Alignment
computer
Algorithms
Zdroj: BMC Bioinformatics, Vol 19, Iss S14, Pp 161-176 (2018)
BMC Bioinformatics
ISSN: 1471-2105
DOI: 10.1186/s12859-018-2389-6
Popis: Background 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. Results 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%. Conclusions 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.
Databáze: OpenAIRE
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