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2018 | OriginalPaper | Buchkapitel

Bi-cluster Parallel Computing in Bioinformatics – Performance and Eco-Efficiency

verfasst von : Paweł Foszner, Przemysław Skurowski

Erschienen in: Parallel Processing and Applied Mathematics

Verlag: Springer International Publishing

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Abstract

The paper discusses the selected bi-clustering algorithms in terms of energy efficiency. We demonstrate the need for the power aware software development, elaborate bi-clustering methods and applications, and describe the experimental computational cluster with a custom built energy measurement instrumentation.

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Literatur
2.
Zurück zum Zitat Ardito, L., Morisio, M.: Green IT - available data and guidelines for reducing energy consumption in IT systems. Sust. Comput.: Inform. Syst. 4(1), 24–32 (2014) Ardito, L., Morisio, M.: Green IT - available data and guidelines for reducing energy consumption in IT systems. Sust. Comput.: Inform. Syst. 4(1), 24–32 (2014)
3.
Zurück zum Zitat Ben-Dor, A., et al.: Discovering local structure in gene expression data: the order-preserving submatrix problem. J. Comput. Biol. 10(3–4), 373–384 (2003)CrossRef Ben-Dor, A., et al.: Discovering local structure in gene expression data: the order-preserving submatrix problem. J. Comput. Biol. 10(3–4), 373–384 (2003)CrossRef
5.
Zurück zum Zitat Cheng, Y., Church, G.M.: Biclustering of expression data. In: Ismb, vol. 8, pp. 93–103 (2000) Cheng, Y., Church, G.M.: Biclustering of expression data. In: Ismb, vol. 8, pp. 93–103 (2000)
6.
Zurück zum Zitat Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc. Ser. B 39 (1977) Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc. Ser. B 39 (1977)
7.
Zurück zum Zitat Feng, W.: Green destiny + mpiBLAST = bioinfomagic. In: 10th International Conference on Parallel Computing (ParCo), September 2003 Feng, W.: Green destiny + mpiBLAST = bioinfomagic. In: 10th International Conference on Parallel Computing (ParCo), September 2003
9.
10.
Zurück zum Zitat Ganote, C.L., et al.: A voice for bioinformatics. In: Proceedings of the PEARC 2017, pp. 36:1–36:5 (2017) Ganote, C.L., et al.: A voice for bioinformatics. In: Proceedings of the PEARC 2017, pp. 36:1–36:5 (2017)
11.
Zurück zum Zitat Gelenbe, E., Caseau, Y.: The impact of information technology on energy consumption and carbon emissions. Ubiquity 2015(June), 1:1–1:15 (2015) Gelenbe, E., Caseau, Y.: The impact of information technology on energy consumption and carbon emissions. Ubiquity 2015(June), 1:1–1:15 (2015)
12.
Zurück zum Zitat Hanczar, B., Nadif, M.: Ensemble methods for biclustering tasks. Pattern Recogn. 45(11), 3938–3949 (2012)CrossRef Hanczar, B., Nadif, M.: Ensemble methods for biclustering tasks. Pattern Recogn. 45(11), 3938–3949 (2012)CrossRef
13.
Zurück zum Zitat Hartigan, J.A.: Direct clustering of a data matrix. JASA 67(337), 123–129 (1972)CrossRef Hartigan, J.A.: Direct clustering of a data matrix. JASA 67(337), 123–129 (1972)CrossRef
14.
Zurück zum Zitat Hibbs, M.A., et al.: Exploring the functional landscape of gene expression: directed search of large microarray compendia. Bioinformatics 23(20), 2692–2699 (2007)CrossRef Hibbs, M.A., et al.: Exploring the functional landscape of gene expression: directed search of large microarray compendia. Bioinformatics 23(20), 2692–2699 (2007)CrossRef
15.
Zurück zum Zitat Hill, M.D., Marty, M.R.: Amdahl’s law in the multicore era. Computer 41, 33–38 (2008)CrossRef Hill, M.D., Marty, M.R.: Amdahl’s law in the multicore era. Computer 41, 33–38 (2008)CrossRef
16.
Zurück zum Zitat Hofmann, T.: Unsupervised learning by probabilistic latent semantic analysis. Mach. Learn. J. 177–196 (2001) Hofmann, T.: Unsupervised learning by probabilistic latent semantic analysis. Mach. Learn. J. 177–196 (2001)
17.
18.
Zurück zum Zitat Kluger, Y., et al.: Spectral biclustering of microarray data: coclustering genes and conditions. Genome Res. 13(4), 703–716 (2003)CrossRef Kluger, Y., et al.: Spectral biclustering of microarray data: coclustering genes and conditions. Genome Res. 13(4), 703–716 (2003)CrossRef
19.
Zurück zum Zitat Kong, M., Partoens, B., Peeters, F.: Structural, dynamical and melting properties of two-dimensional clusters of complex plasmas. New J. Phys. 5(1), 23 (2003)CrossRef Kong, M., Partoens, B., Peeters, F.: Structural, dynamical and melting properties of two-dimensional clusters of complex plasmas. New J. Phys. 5(1), 23 (2003)CrossRef
20.
Zurück zum Zitat Lazzeroni, L., Owen, A.: Plaid models for gene expression data. Statistica sinica 61–86 (2002) Lazzeroni, L., Owen, A.: Plaid models for gene expression data. Statistica sinica 61–86 (2002)
21.
Zurück zum Zitat Lee, D., Seung, S.: Algorithms for non-negative matrix factorization. In: Advances in Neural Information Processing Systems, pp. 556–562 (2000) Lee, D., Seung, S.: Algorithms for non-negative matrix factorization. In: Advances in Neural Information Processing Systems, pp. 556–562 (2000)
22.
Zurück zum Zitat Madeira, S.C., Oliveira, A.L.: Biclustering algorithms for biological data analysis: a survey. IEEE/ACM Trans. Comput. Biol. Bioinform. 24–45 (2004) Madeira, S.C., Oliveira, A.L.: Biclustering algorithms for biological data analysis: a survey. IEEE/ACM Trans. Comput. Biol. Bioinform. 24–45 (2004)
23.
Zurück zum Zitat Mair, J., et al.: Myths in power estimation with performance monitoring counters. Sust. Comput.: Inform. Syst. 4(2), 83–93 (2014) Mair, J., et al.: Myths in power estimation with performance monitoring counters. Sust. Comput.: Inform. Syst. 4(2), 83–93 (2014)
24.
Zurück zum Zitat Markatos, E.P., LeBlanc, T.J.: Load balancing vs. locality management in shared-memory multiprocessors. Technical report, Rochester, NY, USA (1991) Markatos, E.P., LeBlanc, T.J.: Load balancing vs. locality management in shared-memory multiprocessors. Technical report, Rochester, NY, USA (1991)
25.
Zurück zum Zitat Maulik, U., et al.: Multiobjective fuzzy biclustering in microarray data: method and a new performance measure. In: IEEE World Congress on Computational Intelligence Evolutionary Computation, CEC 2008, pp. 1536–1543. IEEE (2008) Maulik, U., et al.: Multiobjective fuzzy biclustering in microarray data: method and a new performance measure. In: IEEE World Congress on Computational Intelligence Evolutionary Computation, CEC 2008, pp. 1536–1543. IEEE (2008)
26.
27.
Zurück zum Zitat Myers, J.L., Well, A.D.: Research Design and Statistical Analysis (ed.) (2003) Myers, J.L., Well, A.D.: Research Design and Statistical Analysis (ed.) (2003)
28.
29.
Zurück zum Zitat Pascual-Montano, A., et al.: Non-smooth non-negative matrix factorization. IEEE Trans. Pattern Anal. Mach. Intell. 403–415 (2006) Pascual-Montano, A., et al.: Non-smooth non-negative matrix factorization. IEEE Trans. Pattern Anal. Mach. Intell. 403–415 (2006)
30.
Zurück zum Zitat Rzepka, K., et al.: Design of portable power consumption measuring system for green computing needs. Studia Informatica (in press). arXiv:1512.08201 [cs] Rzepka, K., et al.: Design of portable power consumption measuring system for green computing needs. Studia Informatica (in press). arXiv:​1512.​08201 [cs]
31.
Zurück zum Zitat Skurowski, P., Staniszewski, M.: Parallel distance matrix computation for matlab data mining. In: AIP Conference Proceedings, vol. 1738, no. 1, p. 070004 (2016) Skurowski, P., Staniszewski, M.: Parallel distance matrix computation for matlab data mining. In: AIP Conference Proceedings, vol. 1738, no. 1, p. 070004 (2016)
32.
Zurück zum Zitat Tanay, A., Sharan, R., Shamir, R.: Discovering statistically significant biclusters in gene expression data. Bioinformatics 18(Suppl. 1), S136–S144 (2002)CrossRef Tanay, A., Sharan, R., Shamir, R.: Discovering statistically significant biclusters in gene expression data. Bioinformatics 18(Suppl. 1), S136–S144 (2002)CrossRef
33.
Zurück zum Zitat Tanay, A., et al.: Revealing modularity and organization in the yeast molecular network by integrated analysis of highly heterogeneous genomewide data. PNAS 101(9), 2981–2986 (2004)CrossRef Tanay, A., et al.: Revealing modularity and organization in the yeast molecular network by integrated analysis of highly heterogeneous genomewide data. PNAS 101(9), 2981–2986 (2004)CrossRef
34.
Zurück zum Zitat Teng, L., Chan, L.: Discovering biclusters by iteratively sorting with weighted correlation coefficient in gene expression data. J. Sig. Proc. Syst. 1520–1527 (2010) Teng, L., Chan, L.: Discovering biclusters by iteratively sorting with weighted correlation coefficient in gene expression data. J. Sig. Proc. Syst. 1520–1527 (2010)
35.
Zurück zum Zitat Yang, J., et al.: \(\delta \)-clusters: capturing subspace correlation in a large data set. In: Proceedings of 18th International Conference Data Engineering, pp. 517–528. IEEE (2002) Yang, J., et al.: \(\delta \)-clusters: capturing subspace correlation in a large data set. In: Proceedings of 18th International Conference Data Engineering, pp. 517–528. IEEE (2002)
Metadaten
Titel
Bi-cluster Parallel Computing in Bioinformatics – Performance and Eco-Efficiency
verfasst von
Paweł Foszner
Przemysław Skurowski
Copyright-Jahr
2018
DOI
https://doi.org/10.1007/978-3-319-78054-2_10