Performance evaluation of parallelization of genetic sequencing GPU
Abstract
How to increase the performance of parallel systems has attracted much research for the construction of new systems on a large scale. In response to this challenge, this article proposes a performance evaluation of DNA research applications in CPU and GPU.
Keywords:
Dedicated architectures and Specific (GPUs, FPGAs, and others)
References
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Edgar, R. C. and Batzoglou, S. (2006). Multiple sequence alignment. Current opinion in structural biology, 16(3):368–373.
Martínez, V., Serpa, M., Navaux, P. O. A., Padoin, E. L., and Panetta, J. (2018). Performance prediction of geophysics numerical kernels on accelerator architectures. In The Eighth International Conference on Smart Grids, Green Communications and IT Energy-aware Technologies (ENERGY 2018), pages 1–6, Nice - França.
Mount, D. W. (2004). Bioinformatics: sequence and genome analysis. 2nd, volume 692. Cold Spring Harbor, NY: Cold Spring Harbor Laboratory Press. xii.
Padoin, E. L., Pilla, L. L., Boito, F. Z., Kassick, R. V., Velho, P., and Navaux, P. O. A. (2013). Evaluating application performance and energy consumption on hybrid CPU+GPU architecture. Cluster Computing, 16(3):511–525. 10.1007/s10586-012-0219-6.
Pavan, P. J., Serpa, M., Carreno, E. D., Martínez, V., Padoin, E. L., Navaux, P., Panetta, J., and Méhaut, J.-F. (2018). Improving performance and energy efficiency of geophysics applications on gpu architectures. High Performance Computing: 5th Latin American Conference, CARLA 2018, Bucaramanga, pages 1–11.
Rastogi, P. and Guddeti, R. (2014). Gpu accelerated inexact matching for multiple patterns in dna sequences. In 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pages 163–167.
Samsi, S., Helfer, B., Kepner, J., Reuther, A., and Ricke, D. O. (2017). A linear algebra approach to fast dna mixture analysis using gpus. In 2017 IEEE High Performance Extreme Computing Conference (HPEC), pages 1–6.
Batzoglou, S. (2005). The many faces of sequence alignment. Briefings in bioinformatics, 6(1):6–22.
Edgar, R. C. and Batzoglou, S. (2006). Multiple sequence alignment. Current opinion in structural biology, 16(3):368–373.
Martínez, V., Serpa, M., Navaux, P. O. A., Padoin, E. L., and Panetta, J. (2018). Performance prediction of geophysics numerical kernels on accelerator architectures. In The Eighth International Conference on Smart Grids, Green Communications and IT Energy-aware Technologies (ENERGY 2018), pages 1–6, Nice - França.
Mount, D. W. (2004). Bioinformatics: sequence and genome analysis. 2nd, volume 692. Cold Spring Harbor, NY: Cold Spring Harbor Laboratory Press. xii.
Padoin, E. L., Pilla, L. L., Boito, F. Z., Kassick, R. V., Velho, P., and Navaux, P. O. A. (2013). Evaluating application performance and energy consumption on hybrid CPU+GPU architecture. Cluster Computing, 16(3):511–525. 10.1007/s10586-012-0219-6.
Pavan, P. J., Serpa, M., Carreno, E. D., Martínez, V., Padoin, E. L., Navaux, P., Panetta, J., and Méhaut, J.-F. (2018). Improving performance and energy efficiency of geophysics applications on gpu architectures. High Performance Computing: 5th Latin American Conference, CARLA 2018, Bucaramanga, pages 1–11.
Rastogi, P. and Guddeti, R. (2014). Gpu accelerated inexact matching for multiple patterns in dna sequences. In 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pages 163–167.
Samsi, S., Helfer, B., Kepner, J., Reuther, A., and Ricke, D. O. (2017). A linear algebra approach to fast dna mixture analysis using gpus. In 2017 IEEE High Performance Extreme Computing Conference (HPEC), pages 1–6.
Published
2020-04-15
How to Cite
KÜNAS, Cristiano Alex; SCHULZE, Vinicios Dutra; PADOIN, Edson Luiz.
Performance evaluation of parallelization of genetic sequencing GPU. In: REGIONAL SCHOOL OF HIGH PERFORMANCE COMPUTING FROM SOUTHERN BRAZIL (ERAD-RS), 20. , 2020, Santa Maria.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 13-16.
ISSN 2595-4164.
DOI: https://doi.org/10.5753/eradrs.2020.10744.
