Running many molecular dynamics simulations on many supercomputers

ACM International Conference Proceeding Series

Volumn , Issue , 2012, Pages

  Mukherjee, Rajib ^a   Thota, Abhinav ^b   Fujioka, Hideki ^c   Bishop, Thomas C ^a   Jha, Shantenu ^d  

^a LOUISIANA TECH UNIVERSITY   (United States)

^b INDIANA UNIVERSITY   (United States)

^c TULANE UNIVERSITY   (United States)

^d RUTGERS UNIVERSITY   (United States)

Author keywords

distributed computing; experience; HPC; large scale; MD; NAMD; technology; XSEDE resources

Indexed keywords

EXPERIENCE; HPC; LARGE SCALE; NAMD; XSEDE RESOURCES;

COUPLINGS; DISTRIBUTED COMPUTER SYSTEMS; MENDELEVIUM; MOLECULAR DYNAMICS; QUEUEING THEORY; SUPERCOMPUTERS; TECHNOLOGY;

IMPORTANCE SAMPLING;

EID: 84865315484     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2335755.2335787     Document Type: Conference Paper

References (19)

1. SAGA-BigJob. https://github.com/saga-project/BigJob/wiki.
2. M. Balman, I. Suslu, and T. Kosar. Distributed data management with petashare. In Proceedings of the 15th ACM Mardi Gras conference: From lightweight mash-ups to lambda grids: Understanding the spectrum of distributed computing requirements, applications, tools, infrastructures, interoperability, and the incremental adoption of key capabilities, MG '08, pages 19:1-19:1, New York, NY, USA, 2008. ACM.
3. C. Chipot and J. Henin. Exploring the free-energy landscape of a short peptide using an average force. The Journal of Chemical Physics, 123(24):244906, 2005.
4. Y. Cui, K. B. Olsen, T. H. Jordan, K. Lee, J. Zhou, P. Small, D. Roten, G. Ely, D. K. Panda, A. Chourasia, J. Levesque, S. M. Day, and P. Maechling. Scalable earthquake simulation on petascale supercomputers. In Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis, SC '10, pages 1-20, Washington, DC, USA, 2010. IEEE Computer Society.
5. E. Darve and A. Pohorille. Calculating free energies using average force. The Journal of Chemical Physics, 115(20):9169-9183, 2001.
6. D. Earl and M. Deem. Parallel tempering: Theory, applications, and new perspectives. PHYSICAL CHEMISTRY CHEMICAL PHYSICS, 7(23):3910-3916, 2005.
7. F. Gagliardi, B. Jones, F. Grey, M.-E. BâĹŽÂ l'gin, and M. Heikkurinen. Building an infrastructure for scientific grid computing: status and goals of the egee project. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 363(1833):1729-1742, 2005.
8. M. Gower, J. Cohen, J. Phillips, R. Kufrin, and K. Schulten. Managing biomolecular simulations in a grid environment with namd-g. In Proceedings of the 2006 TeraGrid Conference, 2006., 2006.
9. U. Hansmann. Parallel tempering algorithm for conformational studies of biological molecules. CHEMICAL PHYSICS LETTERS, 281(1-3):140-150, DEC 19 1997.
10. N. Jacq, V. Breton, H.-Y. Chen, L.-Y. Ho, M. Hofmann, V. Kasam, H.-C. Lee, Y. LegrÃĹ, S. C. Lin, A. MaaïňĆ, E. Medernach, I. Merelli, L. Milanesi, G. Rastelli, M. Reichstadt, J. Salzemann, H. Schwichtenberg, Y.-T. Wu, and M. Zimmermann. Virtual screening on large scale grids. Parallel Computing, 33(4-5):289-301, 2007. Large Scale Grids.
11. Y. E. Khamra and S. Jha. Title: Developing Autonomic Distributed Scientific Applications: A Case Study From History Matching Using Ensemble Kalman-Filters. In GMAC '09: Proceedings of the 6th international conference industry session on Grids meets autonomic computing, pages 19-28, New York, NY, USA, 2009. IEEE.
12. S.-H. Ko, N. Kim, J. Kim, A. Thota, and S. Jha. Efficient runtime environment for coupled multi-physics simulations: Dynamic resource allocation and load-balancing. In Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, CCGRID '10, pages 349-358, Washington, DC, USA, 2010. IEEE Computer Society.
13. S. M. Larson, C. D. Snow, M. Shirts, V. S. P, and V. S. Pande. Folding@home and genome@home: Using distributed computing to tackle previously intractable problems in computational biology.
14. H.-C. Lee, J. Salzemann, N. Jacq, H.-Y. Chen, L.-Y. Ho, I. Merelli, L. Milanesi, V. Breton, S. Lin, and Y.-T. Wu. Grid-enabled high-throughput in silico screening against influenza a neuraminidase. NanoBioscience, IEEE Transactions on, 5(4):288-295, dec. 2006.
15. A. Luckow, L. Lacinski, and S. Jha. SAGA BigJob: An Extensible and Interoperable Pilot-Job Abstraction for Distributed Applications and Systems. In The 10th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, pages 135-144, 2010.
16. J. Phillips, R. Braun, W. Wang, J. Gumbart, E. Tajkhorshid, E. Villa, C. Chipot, R. Skeel, L. Kale, and K. Schulten. Scalable molecular dynamics with NAMD. Journal of Computational Chemistry, 26:1781-1802, 2005.
17. J. Roy and C. A. Laughton. Long-timescale molecular-dynamics simulations of the major urinary protein provide atomistic interpretations of the unusual thermodynamics ÂňâǍǎ of ligand binding. Biophysical Journal, 99(1):218-226, 2010.
18. Y. Sugita and Y. Okamoto. Replica-exchange molecular dynamics method for protein folding. CHEMICAL PHYSICS LETTERS, 314(1-2):141-151, NOV 26 1999.
19. A. Thota, A. Luckow, and S. Jha. Efficient Large-Scale Replica-Exchange Simulations on Production Infrastructure.