Abstract
We report the first congruent integration of HPC, AI, and multiscale modeling (MSM) for solving a mainstream biomechanical problem of thrombogenesis involving 6 million particles at record molecular-scale resolutions in space and at simulation rates of milliseconds per day. The two supercomputers, the IBM Summit-like AiMOS and our University’s SeaWulf, are used for scalability analysis of, and production runs with, the LAMMPS with our customization and AI augmentation and they attained optimal simulation speeds of 3,077 µs/day and 266 µs/day respectively. The long-time and large scales simulations enable the first study of the integrated platelet flowing, flipping, aggregating dynamics in one dynamically-coupled production run. The platelets’ angular and translational speeds, membrane particles’ speeds, and the membrane stress distributions are presented for the analysis of platelets’ aggregations.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Hodak, H.: The nobel prize in chemistry 2013 for the development of multiscale models of complex chemical systems: a tribute to Martin Karplus, Michael Levitt and Arieh Warshel. J. Mol. Biol. 426(1), 1–3 (2014). https://doi.org/10.1016/j.jmb.2013.10.037. ISSN 0022-2836
Alber, M., et al.: Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences. NPJ. Digit. Med. 2, 1–11 (2019)
Virani, S.S., et al.: Heart disease and stroke statistics—2020 update: a report from the American Heart Association. Circulation E139-E596 (2020)
Bluestein, D., Yin, W., Affeld, K., Jesty, J.: Flow-induced platelet activation in a mechanical heart valve. J. Heart Valve Dis. 13, 501–508 (2004)
Poor, H.D., et al.: COVID‐19 critical illness pathophysiology driven by diffuse pulmonary thrombi and pulmonary endothelial dysfunction responsive to thrombolysis. Clin. Transl. Med. 10, e44 (2020)
Rapkiewicz, A.V., et al.: Megakaryocytes and platelet-fibrin thrombi characterize multi-organ thrombosis at autopsy in COVID-19: a case series. EClinicalMedicine 24, 100434 (2020)
Wang, W., King, M.R.: Multiscale modeling of platelet adhesion and thrombus growth. Ann. Biomed. Eng. 40, 2345–2354 (2012)
Zhang, P., Gao, C., Zhang, N., Slepian, M.J., Deng, Y., Bluestein, D.: Multiscale particle-based modeling of flowing platelets in blood plasma using dissipative particle dynamics and coarse grained molecular dynamics. Cell. Mol. Bioeng. 7, 552–574 (2014)
Han, C., Zhang, P., Bluestein, D., Cong, G., Deng, Y.: Artificial intelligence for accelerating time integrations in multiscale modeling. J. Comput. Phys. 427, 110053 (2021)
Dror, R.O., Dirks, R.M., Grossman, J., Xu, H., Shaw, D.E.: Biomolecular simulation: a computational microscope for molecular biology. Annu. Rev. Biophys. 41, 429–452 (2012)
Shaw, D.E., et al.: Anton, a special-purpose machine for molecular dynamics simulation. Commun. ACM 51, 91–97 (2008)
Shaw, D.E., et al.: Anton 2: raising the bar for performance and programmability in a special-purpose molecular dynamics supercomputer. In: SC 2014: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 41–53 (2014)
Yang, C., et al.: Fully integrated FPGA molecular dynamics simulations. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1–31 (2019)
Zhang, T.: SW_GROMACS: accelerate GROMACS on sunway TaihuLight. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1–14 (2019)
Jia, W., et al.: Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1–14 (2020)
Jackson, S.P.: The growing complexity of platelet aggregation. Blood 109, 5087–5095 (2007)
Fogelson, A.L., Guy, R.D.: Immersed-boundary-type models of intravascular platelet aggregation. Comput. Methods Appl. Mech. Eng. 197, 2087–2104 (2008)
Sweet, C.R., Chatterjee, S., Xu, Z., Bisordi, K., Rosen, E.D., Alber, M.: Modelling platelet–blood flow interaction using the subcellular element Langevin method. J. R. Soc. Interface 8, 1760–1771 (2011)
Grinberg, L., et al.: A new computational paradigm in multiscale simulations: application to brain blood flow. In: Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1–5 (2011)
Wu, Z., Xu, Z., Kim, O., Alber, M.: Three-dimensional multi-scale model of deformable platelets adhesion to vessel wall in blood flow. Philos. Trans. Royal Soc. A Math. Phys. Eng. Sci. 372, 20130380 (2014)
Mody, N.A., King, M.R.: Platelet adhesive dynamics. Part I: characterization of platelet hydrodynamic collisions and wall effects. Biophys. J. 95, 2539–2555 (2008)
Mody, N.A., King, M.R.: Platelet adhesive dynamics. Part II: high shear-induced transient aggregation via GPIbα-vWF-GPIbα bridging. Biophys. J. 95, 2556–2574 (2008)
Shiozaki, S., Takagi, S., Goto, S.: Prediction of molecular interaction between platelet glycoprotein Ibα and von Willebrand factor using molecular dynamics simulations. J. Atheroscl. Thrombosis 32458 (2015)
Zhang, P., Zhang, L., Slepian, M.J., Deng, Y., Bluestein, D.: A multiscale biomechanical model of platelets: Correlating with in-vitro results. J. Biomech. 50, 26–33 (2017)
Gupta, P., Zhang, P., Sheriff, J., Bluestein, D., Deng, Y.: A multiscale model for recruitment aggregation of platelets by correlating with in vitro results. Cell. Mol. Bioeng. 12, 327–343 (2019)
Zhang, P., Zhang, N., Deng, Y., Bluestein, D.: A multiple time stepping algorithm for efficient multiscale modeling of platelets flowing in blood plasma. J. Comput. Phys. 284, 668–686 (2015)
Han, C., Zhang, P., Deng, Y.: AI-guided adaptive multiscale modeling of platelet dynamics. In: ACM Student Research Competition Poster of the International Conference for High Performance Computing, Networking, Storage and Analysis (2020)
Hanson, W.A.: The CORAL supercomputer systems. IBM J. Res. Dev. 64, 1:1–1:10 (2019)
Sheriff, J., Bluestein, D.: Platelet dynamics in blood flow. In: Dynamics of Blood Cell Suspensions in Microflows, pp. 215–256. CRC Press (2019)
Slepian, M.J., et al.: Shear-mediated platelet activation in the free flow: perspectives on the emerging spectrum of cell mechanobiological mechanisms mediating cardiovascular implant thrombosis. J. Biomech. 50, 20–25 (2017)
Acknowledgement
The project is supported by the SUNY-IBM Consortium Award, IPDyna: Intelligent Platelet Dynamics, FP00004096 (PI: Y. Deng, Co-PI: P. Zhang). The simulations were conducted on the AiMOS at Rensselaer Polytechnic Institute and the SeaWulf at Stony Brook University.
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
Zhu, Y., Zhang, P., Han, C., Cong, G., Deng, Y. (2021). Enabling AI-Accelerated Multiscale Modeling of Thrombogenesis at Millisecond and Molecular Resolutions on Supercomputers. In: Chamberlain, B.L., Varbanescu, AL., Ltaief, H., Luszczek, P. (eds) High Performance Computing. ISC High Performance 2021. Lecture Notes in Computer Science(), vol 12728. Springer, Cham. https://doi.org/10.1007/978-3-030-78713-4_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-78713-4_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-78712-7
Online ISBN: 978-3-030-78713-4
eBook Packages: Computer ScienceComputer Science (R0)