Comparison of Acceleration Techniques for Selected Low-Level Bioinformatics Operations
Langenkämper D, Jakobi T, Feld D, Jelonek L, Goesmann A, Nattkemper TW (2016)
Frontiers in Genetics 7: 5.
Zeitschriftenaufsatz
| Veröffentlicht | Englisch
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Autor*in
Langenkämper, DanielUniBi 
;
Jakobi, Tobias;
Feld, Dustin;
Jelonek, LukasUniBi;
Goesmann, Alexander;
Nattkemper, Tim WilhelmUniBi
;
Jakobi, Tobias;
Feld, Dustin;
Jelonek, LukasUniBi;
Goesmann, Alexander;
Nattkemper, Tim WilhelmUniBi Einrichtung
Abstract / Bemerkung
Within the recent years clock rates of modern processors stagnated while the demand for computing power continued to grow. This applied particularly for the fields of life sciences and bioinformatics, where new technologies keep on creating rapidly growing piles of raw data with increasing speed. The number of cores per processor increased in an attempt to compensate for slight increments of clock rates. This technological shift demands changes in software development, especially in the field of high performance computing where parallelization techniques are gaining in importance due to the pressing issue of large sized datasets generated by e.g., modern genomics. This paper presents an overview of state-of-the-art manual and automatic acceleration techniques and lists some applications employing these in different areas of sequence informatics. Furthermore, we provide examples for automatic acceleration of two use cases to show typical problems and gains of transforming a serial application to a parallel one. The paper should aid the reader in deciding for a certain techniques for the problem at hand. We compare four different state-of-the-art automatic acceleration approaches (OpenMP, PluTo-SICA, PPCG, and OpenACC). Their performance as well as their applicability for selected use cases is discussed. While optimizations targeting the CPU worked better in the complex k-mer use case, optimizers for Graphics Processing Units (GPUs) performed better in the matrix multiplication example. But performance is only superior at a certain problem size due to data migration overhead. We show that automatic code parallelization is feasible with current compiler software and yields significant increases in execution speed. Automatic optimizers for CPU are mature and usually no additional manual adjustment is required. In contrast, some automatic parallelizers targeting GPUs still lack maturity and are limited to simple statements and structures.
Stichworte
FPGA;
GPU;
automatic;
bioinformatics;
high throughput;
multi-core;
parallelization;
sequence analysis
Erscheinungsjahr
2016
Zeitschriftentitel
Frontiers in Genetics
Band
7
Art.-Nr.
5
ISSN
1664-8021
Page URI
https://pub.uni-bielefeld.de/record/2900943
Zitieren
Langenkämper D, Jakobi T, Feld D, Jelonek L, Goesmann A, Nattkemper TW. Comparison of Acceleration Techniques for Selected Low-Level Bioinformatics Operations. Frontiers in Genetics. 2016;7: 5.
Langenkämper, D., Jakobi, T., Feld, D., Jelonek, L., Goesmann, A., & Nattkemper, T. W. (2016). Comparison of Acceleration Techniques for Selected Low-Level Bioinformatics Operations. Frontiers in Genetics, 7, 5. doi:10.3389/fgene.2016.00005
Langenkämper, Daniel, Jakobi, Tobias, Feld, Dustin, Jelonek, Lukas, Goesmann, Alexander, and Nattkemper, Tim Wilhelm. 2016. “Comparison of Acceleration Techniques for Selected Low-Level Bioinformatics Operations”. Frontiers in Genetics 7: 5.
Langenkämper, D., Jakobi, T., Feld, D., Jelonek, L., Goesmann, A., and Nattkemper, T. W. (2016). Comparison of Acceleration Techniques for Selected Low-Level Bioinformatics Operations. Frontiers in Genetics 7:5.
Langenkämper, D., et al., 2016. Comparison of Acceleration Techniques for Selected Low-Level Bioinformatics Operations. Frontiers in Genetics, 7: 5.
D. Langenkämper, et al., “Comparison of Acceleration Techniques for Selected Low-Level Bioinformatics Operations”, Frontiers in Genetics, vol. 7, 2016, : 5.
Langenkämper, D., Jakobi, T., Feld, D., Jelonek, L., Goesmann, A., Nattkemper, T.W.: Comparison of Acceleration Techniques for Selected Low-Level Bioinformatics Operations. Frontiers in Genetics. 7, : 5 (2016).
Langenkämper, Daniel, Jakobi, Tobias, Feld, Dustin, Jelonek, Lukas, Goesmann, Alexander, and Nattkemper, Tim Wilhelm. “Comparison of Acceleration Techniques for Selected Low-Level Bioinformatics Operations”. Frontiers in Genetics 7 (2016): 5.
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Sommer DD, Delcher AL, Salzberg SL, Pop M., BMC Bioinformatics 8(), 2007
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AUTHOR UNKNOWN, 2013
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Designing modular hardware accelerators in c with roccc 2.0
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GPU-BLAST: using graphics processors to accelerate protein sequence alignment.
Vouzis PD, Sahinidis NV., Bioinformatics 27(2), 2010
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PMID: 21088027
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Warren RL, Sutton GG, Jones SJ, Holt RA., Bioinformatics 23(4), 2006
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Wu TD, Watanabe CK., Bioinformatics 21(9), 2005
PMID: 15728110
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Slurm: simple linux utility for resource management
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Yoo A., Jette M., Grondona M.., 2003
Velvet: algorithms for de novo short read assembly using de Bruijn graphs.
Zerbino DR, Birney E., Genome Res. 18(5), 2008
PMID: 18349386
Zerbino DR, Birney E., Genome Res. 18(5), 2008
PMID: 18349386
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Zhao K, Chu X., Bioinformatics 30(10), 2014
PMID: 24463183
Zhao K, Chu X., Bioinformatics 30(10), 2014
PMID: 24463183
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