Encyclopedia of Parallel Computing

2011 Edition
| Editors: David Padua

Anton, A Special-Purpose Molecular Simulation Machine

  • Ron O. Dror
  • Cliff Young
  • David E. Shaw
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-09766-4_199

Definition

Anton is a special-purpose supercomputer architecture designed by D. E. Shaw Research to dramatically accelerate molecular dynamics (MD) simulations of biomolecular systems. Anton performs massively parallel computation on a set of identical MD-specific ASICs that interact in a tightly coupled manner using a specialized high-speed communication network. Anton enabled, for the first time, the simulation of proteins at an atomic level of detail for periods on the order of a millisecond – about two orders of magnitude beyond the previous state of the art – allowing the observation of important biochemical phenomena that were previously inaccessible to both computational and experimental study.

Discussion

Introduction

Classical molecular dynamics (MD) simulations give scientists the ability to trace the motions of biological molecules at an atomic level of detail. Although MD simulations have helped yield deep insights into the molecular mechanisms of biological processes in a...

This is a preview of subscription content, log in to check access.

Bibliography

  1. 1.
    Bhatele A, Kumar S, Mei C, Phillips JC, Zheng G, Kalé LV (2008) Overcoming scaling challenges in biomolecular simulations across multiple platforms. In: Proceedings of the IEEE international parallel and distributed processing symposium, MiamiGoogle Scholar
  2. 2.
    Bowers KJ, Chow E, Xu H, Dror RO, Eastwood MP, Gregersen BA, Klepeis JL, Kolossváry I, Moraes MA, Sacerdoti FD, Salmon JK, Shan Y, Shaw DE (2006) Scalable algorithms for molecular dynamics simulations on commodity clusters. In: Proceedings of the ACM/IEEE conference on supercomputing (SC06). IEEE, New YorkGoogle Scholar
  3. 3.
    Chow E, Rendleman CA, Bowers KJ, Dror RO, Hughes DH, Gullingsrud J, Sacerdoti FD, Shaw DE (2008) Desmond performance on a cluster of multicore processors. D. E. Shaw Research Technical Report DESRES/TR-2008-01, New York. http://deshawresearch.com
  4. 4.
    Dror RO, Arlow DH, Borhani DW, Jensen MØ, Piana S, Shaw DE (2009) Identification of two distinct inactive conformations of the \({\beta }_{2}\)-adrenergic receptor reconciles structural and biochemical observations. Proc Natl Acad Sci USA 106:4689–4694CrossRefGoogle Scholar
  5. 5.
    Dror RO, Grossman JP, Mackenzie KM, Towles B, Chow E, Salmon JK, Young C, Bank JA, Batson B, Deneroff MM, Kuskin JS, Larson RH, Moraes MA, Shaw DE (2010) Exploiting 162-nanosecond end-to-end communication latency on Anton. In: Proceedings of the conference for high performance computing, networking, storage and analysis (SC10). IEEE, New YorkGoogle Scholar
  6. 6.
    Ensign DL, Kasson PM, Pande VS (2007) Heterogeneity even at the speed limit of folding: large-scale molecular dynamics study of a fast-folding variant of the villin headpiece. J Mol Biol 374:806–816CrossRefGoogle Scholar
  7. 7.
    Fine RD, Dimmler G, Levinthal C (1991) FASTRUN: a special purpose, hardwired computer for molecular simulation. Proteins 11:242–253CrossRefGoogle Scholar
  8. 8.
    Fitch BG, Rayshubskiy A, Eleftheriou M, Ward TJC, Giampapa ME, Pitman MC, Pitera JW, Swope WC, Germain RS (2008) Blue Matter: scaling of N-body simulations to one atom per node. IBM J Res Dev 52:145CrossRefGoogle Scholar
  9. 9.
    Freddolino PL, Liu F, Gruebele MH, Schulten K (2008) Ten-microsecond MD simulation of a fast-folding WW domain. Biophys J 94:L75–L77CrossRefGoogle Scholar
  10. 10.
    Freddolino PL, Park S, Roux B, Schulten K (2009) Force field bias in protein folding simulations. Biophys J 96:3772–2780CrossRefGoogle Scholar
  11. 11.
    Freddolino P, Schulten K (2009) Common structural transitions in explicit-solvent simulations of villin headpiece folding. Biophys J 97:2338–2347CrossRefGoogle Scholar
  12. 12.
    Grossfield A, Pitman MC, Feller SE, Soubias O, Gawrisch K (2008) Internal hydration increases during activation of the G-protein-coupled receptor rhodopsin. J Mol Biol 381:478–486CrossRefGoogle Scholar
  13. 13.
    Grossman JP, Salmon JK, Ho CR, Ierardi DJ, Towles B, Batson B, Spengler J, Wang SC, Mueller R, Theobald M, Young C, Gagliardo J, Deneroff MM, Dror RO, Shaw DE (2008) Hierarchical simulation-based verification of Anton, a special-purpose parallel machine. In: Proceedings of the 26th IEEE international conference on computer design (ICCD ’08), Lake TahoeGoogle Scholar
  14. 14.
    Grossman JP, Young C, Bank JA, Mackenzie K, Ierardi DJ, Salmon JK, Dror RO, Shaw DE (2008) Simulation and embedded software development for Anton, a parallel machine with heterogeneous multicore ASICs. In: Proceedings of the 6th IEEE/ACM/IFIP international conference on hardware/software codesign and system synthesis (CODES/ISSS ’08)Google Scholar
  15. 15.
    Hess B, Kutzner C, van der Spoel D, Lindahl E (2008) GROMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. J Chem Theor Comput 4:435–447CrossRefGoogle Scholar
  16. 16.
    Ho CR, Theobald M, Batson B, Grossman JP, Wang SC, Gagliardo J, Deneroff MM, Dror RO, Shaw DE (2009) Post-silicon debug using formal verification waypoints. In: Proceedings of the design and verification conference and exhibition (DVCon ’09), San JoseGoogle Scholar
  17. 17.
    Khalili-Araghi F, Gumbart J, Wen P-C, Sotomayor M, Tajkhorshid E, Shulten K (2009) Molecular dynamics simulations of membrane channels and transporters. Curr Opin Struct Biol 19:128–137CrossRefGoogle Scholar
  18. 18.
    Klepeis JL, Lindorff-Larsen K, Dror RO, Shaw DE (2009) Long-timescale molecular dynamics simulations of protein structure and function. Curr Opin Struct Biol 19:120–127CrossRefGoogle Scholar
  19. 19.
    Kuskin JS, Young C, Grossman JP, Batson B, Deneroff MM, Dror RO, Shaw DE (2008) Incorporating flexibility in Anton, a specialized machine for molecular dynamics simulation. In: Proceedings of the 14th annual international symposium on high-performance computer architecture (HPCA ’08). IEEE, New YorkGoogle Scholar
  20. 20.
    Larson RH, Salmon JK, Dror RO, Deneroff MM, Young C, Grossman JP, Shan Y, Klepeis JL, Shaw DE (2008) High-throughput pairwise point interactions in Anton, a specialized machine for molecular dynamics simulation. In: Proceedings of the 14th annual international symposium on high-performance computer architecture (HPCA ’08). IEEE, New YorkGoogle Scholar
  21. 21.
    Luttman E, Ensign DL, Vishal V, Houston M, Rimon N, Øland J, Jayachandran G, Friedrichs MS, Pande VS (2009) Accelerating molecular dynamic simulation on the cell processor and PlayStation 3. J Comput Chem 30:262–274CrossRefGoogle Scholar
  22. 22.
    Martinez-Mayorga K, Pitman MC, Grossfield A, Feller SE, Brown MF (2006) Retinal counterion switch mechanism in vision evaluated by molecular simulations. J Am Chem Soc 128:16502–16503CrossRefGoogle Scholar
  23. 23.
    McCammon JA, Gelin BR, Karplus M (1977) Dynamics of folded proteins. Nature 267:585–590CrossRefGoogle Scholar
  24. 24.
    Pande VS, Baker I, Chapman J, Elmer SP, Khaliq S, Larson SM, Rhee YM, Shirts MR, Snow CD, Sorin EJ, Zagrovic B (2003) Atomistic protein folding simulations on the submillisecond time scale using worldwide distributed computing. Biopolymers 68:91–109CrossRefGoogle Scholar
  25. 25.
    Piana S, Sarkar K, Lindorff-Larsen K, Guo M, Gruebele M, Shaw DE (2011) Computational design and experimental testing of the fastest-folding \(\beta \)-sheet protein. J Mol Biol 405:43–48CrossRefGoogle Scholar
  26. 26.
    Rosenbaum DM, Zhang C, Lyons JA, Holl R, Aragao D, Arlow DH, Rasmussen SGF, Choi H-J, DeVree BT, Sunahara RK, Chae PS, Gellman SH, Dror RO, Shaw DE, Weis WI, Caffrey M, Gmeiner P, Kobilka BK (2011) Structure and function of an irreversible agonist-\({\beta }_{2}\) adrenoceptor complex. Nature 469:236–240CrossRefGoogle Scholar
  27. 27.
    Shan Y, Klepeis JL, Eastwood MP, Dror RO, Shaw DE (2005) Gaussian split Ewald: a fast Ewald mesh method for molecular simulation. J Chem Phys 122:054101CrossRefGoogle Scholar
  28. 28.
    Shaw DE (2005) A fast, scalable method for the parallel evaluation of distance-limited pairwise particle interactions. J Comput Chem 26:1318–1328CrossRefGoogle Scholar
  29. 29.
    Shaw DE, Deneroff MM, Dror RO, Kuskin JS, Larson RH, Salmon JK, Young C, Batson B, Bowers KJ, Chao JC, Eastwood MP, Gagliardo J, Grossman JP, Ho CR, Ierardi DJ, Kolossváry I, Klepeis JL, Layman T, McLeavey C, Moraes MA, Mueller R, Priest EC, Shan Y, Spengler J, Theobald M, Towles B, Wang SC (2007) Anton: a special-purpose machine for molecular dynamics simulation. In: Proceedings of the 34th annual international symposium on computer architecture (ISCA ’07). ACM, New YorkGoogle Scholar
  30. 30.
    Shaw DE, Dror RO, Salmon JK, Grossman JP, Mackenzie KM, Bank JA, Young C, Deneroff MM, Batson B, Bowers KJ, Chow E, Eastwood MP, Ierardi DJ, Klepeis JL, Kuskin JS, Larson RH, Lindorff-Larsen K, Maragakis P, Moraes MA, Piana S, Shan Y, Towles B (2009) Millisecond-scale molecular dynamics simulations on Anton. In: Proceedings of the conference for high performance computing, networking, storage and analysis (SC09). ACM, New YorkGoogle Scholar
  31. 31.
    Shaw DE, Maragakis P, Lindorff-Larsen K, Piana S, Dror RO, Eastwood MP, Bank JA, Jumper JM, Salmon JK, Shan Y, Wriggers W (2010) Atomic-level characterization of the structural dynamics of proteins. Science 330:341–346CrossRefGoogle Scholar
  32. 32.
    Stone JE, Phillips JC, Freddolino PL, Hardy DJ, Trabuco LG, Schulten K (2007) Accelerating molecular modeling applications with graphics processors. J Comput Chem 28:2618–2640CrossRefGoogle Scholar
  33. 33.
    Taiji M, Narumi T, Ohno Y, Futatsugi N, Suengaga A, Takada N, Konagaya A (2003) Protein Explorer: a petaflops special-purpose computer system for molecular dynamics simulations. In: Proceedings of the ACM/IEEE conference on supercomputing (SC ’03), Phoenix, AZ. ACM, New YorkGoogle Scholar
  34. 34.
    Toyoda S, Miyagawa H, Kitamura K, Amisaki T, Hashimoto E, Ikeda H, Kusumi A, Miyakawa N (1999) Development of MD Engine: high-speed accelerator with parallel processor design for molecuar dynamics simulations. J Comput Chem 2:185–199CrossRefGoogle Scholar
  35. 35.
    Young C, Bank JA, Dror RO, Grossman JP, Salmon JK, Shaw DE (2009) A \(32 \times 32 \times 32\), spatially distributed 3D FFT in four microseconds on Anton. In: Proceedings of the conference for high performance computing, networking, storage and analysis (SC09). ACM, New YorkGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Ron O. Dror
    • 1
  • Cliff Young
    • 1
  • David E. Shaw
    • 1
    • 2
  1. 1.D. E. Shaw ResearchNew YorkUSA
  2. 2.Senior Research FellowColumbia UniversityNew YorkUSA