short-paper

Scalable In situ Analysis of Molecular Dynamics Simulations

Authors:

Preeti Malakar,

Christopher Knight,

Venkatram Vishwanath,

Michael E. PapkaAuthors Info & Claims

ISAV'17: Proceedings of the In Situ Infrastructures on Enabling Extreme-Scale Analysis and Visualization

Pages 1 - 6

https://doi.org/10.1145/3144769.3144772

Published: 12 November 2017 Publication History

Abstract

Analysis of scientific simulation data enables scientists to glean insight from simulations. In situ analysis, which can be simultaneously executed with the simulation, mitigates I/O bottlenecks and can accelerate discovery of new phenomena. However, in typical modes of operation, this requires either stalling simulation during analysis phase or transferring data for analysis. We study the scalability challenges of time- and space-shared modes of analyzing large-scale molecular dynamics simulations. We also propose topology-aware mapping for simulation and analysis. We demonstrate the benefits of our approach using LAMMPS code on two supercomputers.

References

[1]

LAMMPS Documentation. http://lammps.sandia.gov/doc/Manual.html.

[2]

ParaView Catalyst. http://catalyst.paraview.org.

[3]

Cray Performance Measurement and Analysis Tools. Technical Report S-2376-63, 2015.

[4]

Hasan Abbasi, Matthew Wolf, Greg Eisenhauer, Scott Klasky, Karsten Schwan, and Fang Zheng. DataStager: scalable data staging services for petascale applications. Cluster Computing, 13(3):277--290, 2010.

Digital Library

[5]

M. P. Allen and D. J. Tildesley. Computer Simulation of Liquids. Oxford Science Publications, 1989.

Digital Library

[6]

U. Ayachit, A. Bauer, E. P. N. Duque, G. Eisenhauer, N. Ferrier, J. Gu, K. E. Jansen, B. Loring, Z. Lukic, S. Menon, D. Morozov, P. O'Leary, R. Ranjan, M. Rasquin, C. P. Stone, V. Vishwanath, G. H. Weber, B. Whitlock, M. Wolf, K. J. Wu, and E. W. Bethel. Performance analysis, design considerations, and applications of extreme-scale in situ infrastructures. In SC16: International Conference for High Performance Computing, Networking, Storage and Analysis, pages 921--932, Nov 2016.

[7]

A. C. Bauer, H. Abbasi, J. Ahrens, H. Childs, B. Geveci, S. Klasky, K. Moreland, P. O'Leary, V. Vishwanath, B. Whitlock, and E. W. Bethel. In Situ Methods, Infrastructures, and Applications on High Performance Computing Platforms. Computer Graphics Forum, 35(3):577--597, 2016.

Digital Library

[8]

J.C. Bennett, H. Abbasi, P.-T. Bremer, R. Grout, A. Gyulassy, Tong Jin, S. Klasky, H. Kolla, M. Parashar, V. Pascucci, P. Pebay, D. Thompson, Hongfeng Yu, Fan Zhang, and J. Chen. Combining in-situ and in-transit processing to enable extreme-scale scientific analysis. In Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, 2012.

Digital Library

[9]

A. Bhatele, G. R. Gupta, L. V. Kale, and I. H. Chung. Automated mapping of regular communication graphs on mesh interconnects. In 2010 International Conference on High Performance Computing, Dec 2010.

[10]

Abhinav Bhatele, Todd Gamblin, Steven H Langer, Peer-Timo Bremer, Erik W Draeger, Bernd Hamann, Katherine E Isaacs, Aaditya G Landge, Joshua A Levine, Valerio Pascucci, et al. Mapping applications with collectives over sub-communicators on torus networks. In High Performance Computing, Networking, Storage and Analysis (SC), 2012 International Conference for, pages 1--11. IEEE, 2012.

[11]

Abhinav Bhatele, Nikhil Jain, Yarden Livnat, Valerio Pascucci, and Peer-Timo Bremer. Analyzing network health and congestion in dragonfly-based supercomputers. In IEEE International Parallel and Distributed Processing Symposium (IPDPS), pages 93--102, 2016.

[12]

W. Michael Brown, Peng Wang, Steven J. Plimpton, and Arnold N. Tharrington. Implementing molecular dynamics on hybrid high performance computers âĂŞ short range forces. Computer Physics Communications, 182(4):898--911, 2011.

[13]

F. Chen, M. Flatken, A. Basermann, A. Gerndt, J. Hetheringthon, T. KrÃijger, G. Matura, and R. W. Nash. Enabling In Situ Pre- and Post-processing for Exascale Hemodynamic Simulations - A Co-design Study with the Sparse Geometry Lattice-Boltzmann Code HemeLB. In 2012 SC Companion: High Performance Computing, Networking Storage and Analysis, pages 662--668, Nov 2012.

[14]

Luiz DeRose, Bill Homer, and Dean Johnson. Detecting Application Load Imbalance on High End Massively Parallel Systems. In Proceedings of the 13th International Euro-Par Conference on Parallel Processing, Euro-Par'07, pages 150--159. Springer-Verlag, 2007.

[15]

Ciprian Docan, Manish Parashar, and Scott Klasky. DataSpaces: An Interaction and Coordination Framework for Coupled Simulation Workflows. In Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing, HPDC '10, pages 25--36, 2010.

Digital Library

[16]

M. Dorier, G. Antoniu, F. Cappello, M. Snir, and L. Orf. Damaris: How to Efficiently Leverage Multicore Parallelism to Achieve Scalable, Jitter-free I/O. In 2012 IEEE International Conference on Cluster Computing, pages 155--163, Sept 2012.

Digital Library

[17]

Matthieu Dorier, Matthieu Dreher, Tom Peterka, Justin M. Wozniak, Gabriel Antoniu, and Bruno Raffin. Lessons learned from building in situ coupling frameworks. In Proceedings of the First Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization, ISAV2015, pages 19--24, New York, NY, USA, 2015. ACM.

Digital Library

[18]

M. Dreher and B. Raffin. A Flexible Framework for Asynchronous in Situ and in Transit Analytics for Scientific Simulations. In Cluster, Cloud and Grid Computing (CCGrid), 2014 14th IEEE/ACM International Symposium on, pages 277--286, May 2014.

Digital Library

[19]

Megan Gilge. IBM System Blue Gene Solution: Blue Gene/Q Application Development. IBM Redbooks, June 2013.

[20]

Salman Habib and et al. The Universe at Extreme Scale: Multi-petaflop Sky Simulation on the BG/Q. In Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, 2012.

Digital Library

[21]

R.A. Haring, M. Ohmacht, T.W. Fox, M.K. Gschwind, D.L. Satterfield, K. Sugavanam, P.W. Coteus, P. Heidelberger, M.A. Blumrich, R.W. Wisniewski, A. Gara, G.L.-T. Chiu, P.A. Boyle, N.H. Chist, and Changhoan Kim. The IBM Blue Gene/Q Compute Chip. Micro, IEEE, 32(2):48--60, 2012.

Digital Library

[22]

Torsten Hoefler, Emmanuel Jeannot, and Guillaume Mercier. An Overview of Process Mapping Techniques and Algorithms in High-Performance Computing. In High Performance Computing on Complex Environments, pages 75--94. Wiley, 2014.

[23]

Tsuyoshi Ichimura, Kohei Fujita, Seizo Tanaka, Muneo Hori, Maddegedara Lalith, Yoshihisa Shizawa, and Hiroshi Kobayashi. Physics-based Urban Earthquake Simulation Enhanced by 10.7 BlnDOF × 30 K Time-step Unstructured FE Nonlinear Seismic Wave Simulation. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, 2014.

Digital Library

[24]

J. Jeffers, J. Reinders, and A. Sodani. Intel Xeon Phi Processor High Performance Programming: Knights Landing Edition. Elsevier Science, 2016.

Digital Library

[25]

Geir Johansen. Managing Cray XT MPI Runtime Environment Variables to Optimize and Scale Applications. In Proceedings of the 2008 Cray User Group meeting, CUG '08, 2008.

[26]

Peter Johnsen, Mark Straka, Melvyn Shapiro, Alan Norton, and Thomas Galarneau. Petascale WRF Simulation of Hurricane Sandy Deployment of NCSA's Cray XE6 Blue Waters. In Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, 2013.

Digital Library

[27]

Gary Lakner and et al. IBM System Blue Gene Solution: Performance Analysis Tools. IBM Redbooks, November 2008.

[28]

Qing Liu, Jeremy Logan, Yuan Tian, Hasan Abbasi, Norbert Podhorszki, Jong Youl Choi, Scott Klasky, Roselyne Tchoua, Jay Lofstead, Ron Oldfield, Manish Parashar, Nagiza Samatova, Karsten Schwan, Arie Shoshani, Matthew Wolf, Kesheng Wu, and Weikuan Yu. Hello adios: the challenges and lessons of developing leadership class i/o frameworks. Concurrency and Computation: Practice and Experience, 26(7):1453--1473, 2014.

Digital Library

[29]

Preeti Malakar, Thomas George, Sameer Kumar, Rashmi Mittal, Vijay Natarajan, Yogish Sabharwal, Vaibhav Saxena, and Sathish S. Vadhiyar. A Divide and Conquer Strategy for Scaling Weather Simulations with Multiple Regions of Interest. In Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, 2012.

Digital Library

[30]

Preeti Malakar, Venkatram Vishwanath, Christopher Knight, Todd Munson, and Michael E. Papka. Optimal execution of co-analysis for large-scale molecular dynamics simulations. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, 2016.

[31]

Preeti Malakar, Venkatram Vishwanath, Todd Munson, Christopher Knight, Mark Hereld, Sven Leyffer, and Michael E. Papka. Optimal scheduling of in-situ analysis for large-scale scientific simulations. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, 2015.

Digital Library

[32]

Patrick OâĂ &Zacute;Leary, James Ahrens, SÃl'bastien Jourdain, Scott Wittenburg, David H. Rogers, and Mark Petersen. Cinema image-based in situ analysis and visualization of MPAS-ocean simulations. Parallel Computing, 55:43--48, 2016. Visualization and Data Analytics for Scientific Discovery.

Digital Library

[33]

T. Peterka, J. Kwan, A. Pope, H. Finkel, K. Heitmann, S. Habib, Jingyuan Wang, and G. Zagaris. Meshing the universe: Integrating analysis in cosmological simulations. In High Performance Computing, Networking, Storage and Analysis (SCC), 2012 SC Companion:, pages 186--195, Nov 2012.

[34]

Steve Plimpton. Fast parallel algorithms for short-range molecular dynamics. Journal of Computational Physics, 117(1):1--19, 1995.

Digital Library

[35]

Amanda Randles, Erik W. Draeger, Tomas Oppelstrup, Liam Krauss, and John A. Gunnels. Massively Parallel Models of the Human Circulatory System. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC '15, 2015.

Digital Library

[36]

C. Seshadhri, Ali Pinar, David Thompson, and JanineC. Bennett. Sublinear algorithms for extreme-scale data analysis. In Janine Bennett, Fabien Vivodtzev, and Valerio Pascucci, editors, Topological and Statistical Methods for Complex Data, Mathematics and Visualization, pages 39--54. Springer Berlin Heidelberg, 2015.

[37]

Christopher Sewell, Katrin Heitmann, Hal Finkel, George Zagaris, Suzanne T. Parete-Koon, Patricia K. Fasel, Adrian Pope, Nicholas Frontiere, Li-ta Lo, Bronson Messer, Salman Habib, and James Ahrens. Large-scale compute-intensive analysis via a combined in-situ and co-scheduling workflow approach. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC '15, 2015.

Digital Library

[38]

Sameer S. Shende and Allen D. Malony. The Tau Parallel Performance System. International Journal of High Performance Computing Applications, 20(2):287--311, 2006.

Digital Library

[39]

A. Sodani, R. Gramunt, J. Corbal, H. S. Kim, K. Vinod, S. Chinthamani, S. Hutsell, R. Agarwal, and Y. C. Liu. Knights Landing: Second-Generation Intel Xeon Phi Product. IEEE Micro, 36(2):34--46, 2016.

Digital Library

[40]

DOE ASCAC Data Subcommittee. Synergistic Challenges in Data-Intensive Science and Exascale Computing. Technical report, March 2013.

[41]

Brad Whitlock, Jean M. Favre, and Jeremy S. Meredith. Parallel in Situ Coupling of Simulation with a Fully Featured Visualization System. In Proceedings of the 11th Eurographics Conference on Parallel Graphics and Visualization, EGPGV '11, pages 101--109, Aire-la-Ville, Switzerland, Switzerland, 2011. Eurographics Association.

Digital Library

[42]

Boyu Zhang, Trilce Estrada, Pietro Cicotti, and Michela Taufer. Enabling In-Situ Data Analysis for Large Protein-Folding Trajectory Datasets. In Proceedings of the 2014 IEEE 28th International Parallel and Distributed Processing Symposium, IPDPS '14, pages 221--230, 2014.

Digital Library

[43]

Fang Zheng, H. Abbasi, C. Docan, J. Lofstead, Qing Liu, S. Klasky, M. Parashar, N. Podhorszki, K. Schwan, and M. Wolf. PreDatA -- preparatory data analytics on peta-scale machines. In Parallel Distributed Processing (IPDPS), 2010 IEEE International Symposium on, April 2010.

[44]

Fang Zheng, Hongbo Zou, G. Eisenhauer, K. Schwan, M. Wolf, J. Dayal, Tuan-Anh Nguyen, Jianting Cao, H. Abbasi, S. Klasky, N. Podhorszki, and Hongfeng Yu. FlexIO: I/O Middleware for Location-Flexible Scientific Data Analytics. In Parallel Distributed Processing (IPDPS), 2013 IEEE 27th International Symposium on, pages 320--331, May 2013.

Digital Library

Cited By

Lanrezac AFérey NBaaden M(2024)Interactive Molecular DynamicsComprehensive Computational Chemistry10.1016/B978-0-12-821978-2.00115-X(454-474)Online publication date: 2024
https://doi.org/10.1016/B978-0-12-821978-2.00115-X
Marincic IVishwanath VHoffmann H(2020)SeeSAw: Optimizing Performance of In-Situ Analytics Applications under Power Constraints2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS)10.1109/IPDPS47924.2020.00086(789-798)Online publication date: May-2020
https://doi.org/10.1109/IPDPS47924.2020.00086
Khoshlessan MParaskevakos IFox GJha SBeckstein O(2020)Parallel performance of molecular dynamics trajectory analysisConcurrency and Computation: Practice and Experience10.1002/cpe.578932:19Online publication date: 27-Apr-2020
https://doi.org/10.1002/cpe.5789
Show More Cited By

Recommendations

MIC acceleration of short-range molecular dynamics simulations
COSMIC '13: Proceedings of the First International Workshop on Code OptimiSation for MultI and many Cores

Heterogeneous systems containing accelerators such as GPUs or co-processors such as Intel MIC are becoming more prevalent due to their ability of exploiting large-scale parallelism in applications. In this paper, we have developed a hierarchical ...
Towards Large-Scale Molecular Dynamics Simulations on Graphics Processors
BICoB '09: Proceedings of the 1st International Conference on Bioinformatics and Computational Biology

Atomistic molecular dynamics (MD) simulations are a vital tool in chemical research, as they are able to provide a view of chemical systems and processes that is not obtainable through experiment. However, large-scale MD simulations require access to ...
Increasing Molecular Dynamics Simulations Throughput by Virtualizing Remote GPUs with rCUDA
ICPP Workshops '18: Workshop Proceedings of the 47th International Conference on Parallel Processing

The full-understanding of the dynamics of molecular systems at the atomic scale is of great relevance in the fields of chemistry physics, materials science and drug discovery just to name a few. Molecular dynamics (MD) is a widely used computer tool for ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

ISAV'17: Proceedings of the In Situ Infrastructures on Enabling Extreme-Scale Analysis and Visualization

November 2017

53 pages

ISBN:9781450351393

DOI:10.1145/3144769

Copyright © 2017 ACM.

Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 November 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Short-paper
Research
Refereed limited

Funding Sources

U.S. Department of Energy, Office of Science

Conference

SC '17

Sponsor:

SIGHPC

SC '17: The International Conference for High Performance Computing, Networking, Storage and Analysis

November 12 - 17, 2017

CO, Denver, USA

Acceptance Rates

ISAV'17 Paper Acceptance Rate 9 of 28 submissions, 32%;

Overall Acceptance Rate 23 of 63 submissions, 37%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
238
Total Downloads

Downloads (Last 12 months)25
Downloads (Last 6 weeks)2

Reflects downloads up to 01 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Lanrezac AFérey NBaaden M(2024)Interactive Molecular DynamicsComprehensive Computational Chemistry10.1016/B978-0-12-821978-2.00115-X(454-474)Online publication date: 2024
https://doi.org/10.1016/B978-0-12-821978-2.00115-X
Marincic IVishwanath VHoffmann H(2020)SeeSAw: Optimizing Performance of In-Situ Analytics Applications under Power Constraints2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS)10.1109/IPDPS47924.2020.00086(789-798)Online publication date: May-2020
https://doi.org/10.1109/IPDPS47924.2020.00086
Khoshlessan MParaskevakos IFox GJha SBeckstein O(2020)Parallel performance of molecular dynamics trajectory analysisConcurrency and Computation: Practice and Experience10.1002/cpe.578932:19Online publication date: 27-Apr-2020
https://doi.org/10.1002/cpe.5789
Ames JRizzi SInsley JPatel SHernaandez BDraeger ERandles A(2019)Low-Overhead In Situ Visualization Using Halo Replay2019 IEEE 9th Symposium on Large Data Analysis and Visualization (LDAV)10.1109/LDAV48142.2019.8944265(16-26)Online publication date: Oct-2019
https://doi.org/10.1109/LDAV48142.2019.8944265

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents