Abstract
This paper addresses the performance evaluation of a heterogeneous distributed computing environment (Desktop Grid) for large-scale medicinal chemistry experiments in silico. Dynamic change of the set of computational nodes, their heterogeneity and unreliability impose difficulties on task scheduling and algorithm scaling. We analyze the performance, provide efficiency metrics, statistics and analysis of the volunteer computing project SiDock@home.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alonso-Monsalve, S., García-Carballeira, F., Calderón, A.: ComBos: a complete simulator of volunteer computing and desktop grids. Simul. Modell. Pract. Theory 77, 197–211 (2017). https://doi.org/10.1016/j.simpat.2017.06.002. https://www.sciencedirect.com/science/article/pii/S1569190X17301028
Anderson, D.P.: BOINC: a platform for volunteer computing. J. Grid Comput. 18(1), 99–122 (2020)
By Editorial Team: The history of supercomputing vs. COVID-19. https://www.hpcwire.com/2021/03/09/the-history-of-supercomputing-vs-covid-19. Accessed 18 Mar 2021
Cai, Y., et al.: LHC@Home: a BOINC-based volunteer computing infrastructure for physics studies at CERN. Open Eng. 7, 379–393 (2017)
Climateprediction.net—the world’s largest climate modelling experiment for the 21st century. https://www.climateprediction.net. Accessed 10 June 2021
Cloud Computing Services—Microsoft Azure. https://azure.microsoft.com/en-us/. Accessed 10 June 2021
CmDock. https://gitlab.com/Jukic/cmdock. Accessed 10 June 2021
CreditNew - BOINC. https://boinc.berkeley.edu/trac/wiki/CreditNew. Accessed 10 June 2021
Curnow, H.J., Wichmann, B.A.: A synthetic benchmark. Comput. J. 19(1), 43–49 (1976)
Distributed Computing – Computing Platforms. http://distributedcomputing.info/platforms.html. Accessed 10 June 2021
Dongarra, J.J., Luszczek, P., Petitet, A.: The LINPACK benchmark: past, present and future. Concurr. Comput.: Pract. Exp. 15(9), 803–820 (2003). https://doi.org/10.1002/cpe.728. https://onlinelibrary.wiley.com/doi/abs/10.1002/cpe.728
Estrada, T., Taufer, M., Anderson, D.P.: Performance prediction and analysis of BOINC projects: an empirical study with EmBOINC. J. Grid Comput. 7(4), 537 (2009)
Home – COVID.SI. https://covid.si/en. Accessed 10 June 2021
Home—LHC@home. https://lhcathome.web.cern.ch. Accessed 10 June 2021
Institute for Protein design, University of Washington: Rosetta’s role in fighting coronavirus. https://www.ipd.uw.edu/2020/02/rosettas-role-in-fighting-coronavirus. Accessed 10 June 2021
Ivashko, E., Chernov, I., Nikitina, N.: A survey of desktop grid scheduling. IEEE Trans. Parallel Distrib. Syst. 29(12), 2882–2895 (2018)
Jukič, M., Janežič, D., Bren, U.: Ensemble docking coupled to linear interaction energy calculations for identification of coronavirus main protease (3CLpro) non-covalent small-molecule inhibitors. Molecules 25(24), 5808 (2020)
Legrand, A.: Scheduling for large scale distributed computing systems: approaches and performance evaluation issues. Habilitation à diriger des recherches, Université Grenoble Alpes, November 2015. https://tel.archives-ouvertes.fr/tel-01247932
Rosetta@home. https://boinc.bakerlab.org. Accessed 10 June 2021
SiDock@home. https://sidock.si/sidock. Accessed 10 June 2021
Suhail, M.: Performance analysis of distributed systems using BOINC. Int. J. Comput. Appl. 975, 8887 (2016)
Together We Are Powerful - Folding@home. https://foldingathome.org. Accessed 10 June 2021
Top50—Supercomputers [in Russian]. http://top50.supercomputers.ru/list. Accessed 10 June 2021
Wrapp, D., et al.: Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation. Science 367(6483), 1260–1263 (2020)
Zaikin, O., Manzyuk, M., Kochemazov, S., Bychkov, I.V., Semenov, A.A.: A volunteer-computing-based grid architecture incorporating idle resources of computational clusters. In: Dimov, I., Faragó, I., Vulkov, L.G. (eds.) Numerical Analysis and Its Applications. Lecture Notes in Computer Science, vol. 10187, pp. 769–776. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-319-57099-0_89
Zimmerman, M.I., et al.: SARS-CoV-2 simulations go exascale to capture spike opening and reveal cryptic pockets across the proteome. bioRxiv (2020). https://doi.org/10.1101/2020.06.27.175430. https://www.biorxiv.org/content/early/2020/10/07/2020.06.27.175430
Acknowledgements
The initial library (one billion of compounds) was prepared with the generous help of Microsoft that donated computational resources in the Azure cloud platform [6]. COVID.SI team is grateful and looking forward to future collaborations.
We wholeheartedly thank all BOINC participants for their contributions.
Funding
This work was partly supported by the Scholarship of the President of the Russian Federation for young scientists and graduate students (project SP-609.2021.5); the Slovenian Ministry of Science and Education infrastructure project grant HPC-RIVR; the Slovenian Research Agency (ARRS) programme P2-0046 and J1-2471, the Physical Chemistry programme grant P1-0201; Slovenian Ministry of Education, Science and Sports programme grant OP20.04342.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Nikitina, N., Manzyuk, M., Podlipnik, Č., Jukić, M. (2021). Performance Estimation of a BOINC-Based Desktop Grid for Large-Scale Molecular Docking. In: Malyshkin, V. (eds) Parallel Computing Technologies. PaCT 2021. Lecture Notes in Computer Science(), vol 12942. Springer, Cham. https://doi.org/10.1007/978-3-030-86359-3_26
Download citation
DOI: https://doi.org/10.1007/978-3-030-86359-3_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-86358-6
Online ISBN: 978-3-030-86359-3
eBook Packages: Computer ScienceComputer Science (R0)