research-article

Iterative induced dipoles computation for molecular mechanics on GPUs

Authors:

Frederico Pratas,

Ricardo A. Mata,

Leonel SousaAuthors Info & Claims

GPGPU-3: Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units

Pages 111 - 120

https://doi.org/10.1145/1735688.1735708

Published: 14 March 2010 Publication History

Abstract

In this work, we present a first step towards the efficient implementation of polarizable molecular mechanics force fields with GPU acceleration. The computational bottleneck of such applications is found in the treatment of electrostatics, where higher-order multipoles and a self-consistent treatment of polarization effects are needed. We have coded these sections, for the case of a non-periodic simulation, with the CUDA programming model. Results show a speedup factor of 21 for a single precision GPU implementation, when comparing to the serial CPU version. A discussion of the optimization and parameterization steps is included. Comparison between different graphic cards and a shared memory parallel CPU implementation is also given. The current work demonstrates the potential usefulness of GPU programming in accelerating this field of applications.

References

[1]

A. Aguado and P. A. Madden. Ewald summation of electrostatic multipole interactions up to the quadrupolar level. J. Chem. Phys., 119:7471, 2003.

[2]

J. A. Anderson, C. D. Lorenz, and A. Travesset. General purpose molecular dynamics simulations fully implemented on graphics processing units. J. Comp. Phys., 227:5342, 2008.

Digital Library

[3]

P. Barnes, J. L. Finney, J. D. Nicholas, et al. Cooperative effects in simulated water. Nature, 282:459, 1979.

[4]

M. D. Beachy, D. Chasman, R. B. Murphy, et al. Accurate ab initio quantum chemical determination of the relative energetics of peptide conformations and assessment of empirical force fields. J. Am. Chem. Soc., 119:5908, 1997.

[5]

C. J. Burnham, J. C. Li, S. S. Xantheas, et al. The parametrization of a thole-type all-atom polarizable water model from first principles and its application to the study of water clusters (n=2âĂŞ21) and the phonon spectrum of ice ih. J. Chem. Phys., 110:4566, 1999.

[6]

R. Chelli, P. Procacci, R. Righini, et al. Electrical response in chemical potential equalization schemes. J. Chem. Phys., 111:8569, 1999.

[7]

B. L. Claus and S. R. Johnson. Grid computing in large pharmaceutical molecular modeling. Drug Discov. Today, 13:578, 2008.

[8]

W. D. Cornell, P. Cieplak, C. I. Bayly, et al. A second generation force field for the simulation of proteins, nucleic acids, and organic molecules. J. Am. Chem. Soc., 117:5179, 1995.

[9]

B. G. Fitch, R. S. Germain, M. Mendell, et al. Blue matter, an application framework for molecular simulation on blue gene. J. Parallel Distrib. Comput., 63:759, 2003.

Digital Library

[10]

M. S. Friederichs, P. Eastman, V. Vaidyanathan, et al. Accelerating molecular dynamic simulation on graphics processing units. J. Comput. Chem., 30:864, 2009.

[11]

K. Group. Open Computing Language (OpenCL), Jan 2010. http://www.khronos.org/opencl/.

[12]

B. Guillot and Y. Guissani. How to build a better pair potential for water. J. Chem. Phys., 114:6720, 2001.

[13]

M. J. Harvey and G. D. Fabritiis. An implementation of the smooth particle mesh ewald method on gpu hardware. J. Chem. Theory Comput., 5:2371, 2009.

[14]

B. Jang, S. Do, H. Pien, et al. Architecture-aware optimization targeting multithreaded stream computing. GPGPU-2: Proceedings of 2nd Workshop on General Purpose Processing on Graphics Processing Units, pages 1--9, Mar 2009.

Digital Library

[15]

W. L. Jorgensen, D. S. Maxwell, and J. Tirado-Rives. Development and testing of the opls all-atom force field on conformational energetics and properties of organic liquids. J. Am. Chem. Soc., 118:11225, 1996.

[16]

J. Kaminsky and F. Jensen. Force field modeling of amino acid conformational energies. J. Chem. Theor. Comput., 3:1774, 2007.

[17]

J. Kaminsky, R. A. Mata, H.-J. Werner, et al. The accuracy of local mp2 methods for conformational energies. Mol. Phys., 106:1899, 2008.

[18]

P. Kongetira, K. Aingaran, and K. Olukotun. Niagara: A 32-way multithreaded sparc processor. IEEE Micro-Institute of Electrical and Electronics Engineers, 25(2):21--29, 2005.

Digital Library

[19]

E. Lindholm, J. Nickolls, and S. o. Oberman. NVIDIA Tesla: A Unified Graphics and Computing Architecture. Micro, IEEE, 28(2):39--55, March 2008.

Digital Library

[20]

D. Luebke, M. Harris, J. Krüger, et al. GPGPU: General Purpose Computation on Graphics Hardware. In SIGGRAPH '04: ACM SIGGRAPH 2004 Course Notes, page 33, New York, NY, USA, 2004. ACM.

Digital Library

[21]

A. D. MacKerell Jr., D. Bashford, M. Bellott, et al. All-atom empirical potential for molecular modeling and dynamics studies of proteins. J. Phys. Chem. B, 102:3586, 1998.

[22]

J. A. V. Meel, A. Arnold, D. Frenkel, et al. Harvesting graphics power for md simulations. Mol. Simul., 34:259, 2008.

[23]

NVIDIA. CUDA occupancy calculator v1.5, Feb 2009. http://developer.download.nvidia.com/compute/cuda//CUDA_Occupancy_calculator.xls.

[24]

NVIDIA. Compute Unified Device Architecture (CUDA), Jan 2010. http://www.nvidia.com/object/cuda_home.html.

[25]

J. W. Ponder. TINKER: Software Tools for Molecular Design; 4.2 ed. Washington University School of Medicine: Saint Louis, 2003.

[26]

J. W. Ponder and D. A. Case. Force fields for protein simulations. Adv. Prot. Chem., 66:27, 2003.

[27]

M. Quinn. Parallel Programming in C with MPI and OpenMP. McGraw-Hill Science Engineering, 2004.

Digital Library

[28]

A. K. Rappé and W. A. Goddard III. Charge equilibration for molecular dynamics simulations. J. Phys. Chem., 95:3358, 1991.

[29]

P. Ren and J. W. Ponder. Consistent treatment of inter- and intramolecular polarization in molecular mechanics calculations. J. Comput. Chem., 23:1497, 2002.

[30]

P. Ren and J. W. Ponder. Polarizable atomic multipole water model for molecular mechanics simulation. J. Phys. Chem. B, 107:5933, 2003.

[31]

S. W. Rick. Simulations of ice and liquid water over a range of temperatures using the fluctuating charge model. J. Chem. Phys., 114:2276, 2001.

[32]

S. W. Rick and B. J. Berne. Dynamical fluctuating charge force fields: the aqueous solvation of amides. J. Am. Chem. Soc., 118:672, 1996.

[33]

S. W. Rick, S. J. Stuart, and B. J. Berne. Dynamical fluctuating charge force fields: Application to liquid water. J. Chem. Phys., 101:6141, 1994.

[34]

S. Ryoo, C. I. Rodrigues, S. S. Stone, et al. Program optimization carving for gpu computing. J Parallel Distr Com, 68(10):1389--1401, Jan 2008.

Digital Library

[35]

H. A. Stern, F. Rittner, B. J. Berne, et al. Combined fluctuating charge and polarizable dipole models: Application to a five-site water potential function. J. Chem. Phys., 115:2237, 2001.

[36]

J. E. Stone, J. C. Phillips, P. L. Freddolino, et al. Accelerating molecular modeling applications with graphics processors. J. Comput. Chem., 28:2618, 2007.

[37]

S. J. Stuart and B. J. Berne. Effects of polarizability on the hydration of the chloride ion. J. Phys. Chem., 100:11934, 1996.

Cited By

Jász ÁRák ÁLadjánszki ICserey G(2019)Classical molecular dynamics on graphics processing unit architecturesWIREs Computational Molecular Science10.1002/wcms.144410:2Online publication date: 5-Sep-2019
https://doi.org/10.1002/wcms.1444
Pratas FTrancoso PSousa LStamatakis AShi GKindratenko V(2018)Fine-grain parallelism using multi-core, Cell/BE, and GPU SystemsParallel Computing10.1016/j.parco.2011.08.00238:8(365-390)Online publication date: 31-Dec-2018
https://dl.acm.org/doi/10.1016/j.parco.2011.08.002
Pratas FTomas PTrancoso PSousa L(2012)Energy efficient stream-based configurable architecture for embedded platforms2012 International Conference on Embedded Computer Systems (SAMOS)10.1109/SAMOS.2012.6404174(193-200)Online publication date: Jul-2012
https://doi.org/10.1109/SAMOS.2012.6404174
Show More Cited By

Index Terms

Iterative induced dipoles computation for molecular mechanics on GPUs
1. Computing methodologies
  1. Parallel computing methodologies
    1. Parallel programming languages
2. Software and its engineering
  1. Software notations and tools
    1. General programming languages
      1. Language types
        Parallel programming languages

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cover image ACM Other conferences

GPGPU-3: Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units

March 2010

124 pages

ISBN:9781605589350

DOI:10.1145/1735688

General Chairs:
David Kaeli
Northeastern University, Boston, MA
,
Miriam Leeser
Northeastern University, Boston, MA

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 14 March 2010

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GPGPU-3

GPGPU-3: Third Workshop on General-Purpose Computation on Graphics Processing Units

March 14, 2010

Pennsylvania, Pittsburgh, USA

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Cited By

Jász ÁRák ÁLadjánszki ICserey G(2019)Classical molecular dynamics on graphics processing unit architecturesWIREs Computational Molecular Science10.1002/wcms.144410:2Online publication date: 5-Sep-2019
https://doi.org/10.1002/wcms.1444
Pratas FTrancoso PSousa LStamatakis AShi GKindratenko V(2018)Fine-grain parallelism using multi-core, Cell/BE, and GPU SystemsParallel Computing10.1016/j.parco.2011.08.00238:8(365-390)Online publication date: 31-Dec-2018
https://dl.acm.org/doi/10.1016/j.parco.2011.08.002
Pratas FTomas PTrancoso PSousa L(2012)Energy efficient stream-based configurable architecture for embedded platforms2012 International Conference on Embedded Computer Systems (SAMOS)10.1109/SAMOS.2012.6404174(193-200)Online publication date: Jul-2012
https://doi.org/10.1109/SAMOS.2012.6404174
Pratas FSousa LDieterich JMata R(2012)Computation of Induced Dipoles in Molecular Mechanics Simulations Using Graphics ProcessorsJournal of Chemical Information and Modeling10.1021/ci200564x52:5(1159-1166)Online publication date: 9-May-2012
https://doi.org/10.1021/ci200564x
Xia YKuang LLi X(2011)Accelerating geospatial analysis on GPUs using CUDAJournal of Zhejiang University SCIENCE C10.1631/jzus.C110005112:12(990-999)Online publication date: 6-Dec-2011
https://doi.org/10.1631/jzus.C1100051

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