
This Article  
 
Share  
Bibliographic References  
Add to:  
Digg Furl Spurl Blink Simpy Del.icio.us Y!MyWeb  
Search  
 
ASCII Text  x  
Kenneth P. Esler, Jeongnim Kim, David M. Ceperley, Luke Shulenburger, "Accelerating Quantum Monte Carlo Simulations of Real Materials on GPU Clusters," Computing in Science and Engineering, vol. 14, no. 1, pp. 4051, Jan.Feb., 2012.  
BibTex  x  
@article{ 10.1109/MCSE.2010.122, author = {Kenneth P. Esler and Jeongnim Kim and David M. Ceperley and Luke Shulenburger}, title = {Accelerating Quantum Monte Carlo Simulations of Real Materials on GPU Clusters}, journal ={Computing in Science and Engineering}, volume = {14}, number = {1}, issn = {15219615}, year = {2012}, pages = {4051}, doi = {http://doi.ieeecomputersociety.org/10.1109/MCSE.2010.122}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  MGZN JO  Computing in Science and Engineering TI  Accelerating Quantum Monte Carlo Simulations of Real Materials on GPU Clusters IS  1 SN  15219615 SP40 EP51 EPD  4051 A1  Kenneth P. Esler, A1  Jeongnim Kim, A1  David M. Ceperley, A1  Luke Shulenburger, PY  2012 KW  Component KW  graphics processors KW  Monte Carlo KW  physics KW  scientific computing VL  14 JA  Computing in Science and Engineering ER   
More accurate than meanfield methods and more scalable than quantum chemical methods, continuum quantum Monte Carlo (QMC) is an invaluable tool for predicting the properties of matter from fundamental principles. Because QMC algorithms offer multiple forms of parallelism, they're ideal candidates for acceleration in the manycore paradigm.
1. Proc of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character, vol. 123, no. 792, 1929.
2. W.M.C. Foulkes et al., "Quantum Monte Carlo Simulations of Solids," Rev. Modern Physics, vol. 73, 2001, pp. 33–83.
3. K.P. Esler et al., "Quantum Monte Carlo Algorithms for Electronic Structure at the Petascale; The Endstation Project," J. Physics: Conf. Series, vol. 125, 2008, p. 012057; doi:10.1088/17426596/125/1/012057.
4. A.G. Anderson, W.A. Goddard, and P. Schroder, "Quantum Monte Carlo on Graphical Processing Units," Computer Physics Comm., vol. 177, no. 3, 2007, pp. 298–306.
5. I.S. Ufimtsev and T.J. Martinez, "Quantum Chemistry on Graphical Processing Units. 3. Analytical Energy Gradients, Geometry Optimization, and First Principles Molecular Dynamics," J. Chemical Theory Computation, vol. 5, no. 10, 2009, pp. 2619–2628.
6. K.P. Esler et al., "Fundamental HighPressure Calibration from AllElectron Quantum Monte Carlo Calculations," Physical Rev. Letters, vol. 104, no. 18, 2010, p. 185702; doi:10.1103/PhysRevLett.104.185702.
7. S. Chiesa, D.M. Ceperley, and S. Zhang, "Accurate, Efficient, and Simple Forces Computed with Quantum Monte Carlo Methods," Physical Rev. Letters, vol. 94, no. 3, 2005, p. 036404; doi:10.1103/PhysRevLett.94.036404.
8. A. Badinski and R.J. Needs, "Total Forces in the Diffusion Monte Carlo Method with Nonlocal Pseudopotentials," Physical Rev. Letters, vol. 78, no. 3, 2008, p. 035134, doi:10.1103/PhysRevB.78.035134.
9. D. Alfé and M.J. Gillan, "Efficient Localized Basis Set for Quantum Monte Carlo Calculations on Condensed Matter," Physics Rev. B., vol. 70, no. 16, 2004, p. 1661101; doi:10.1103/PhysRevB.70.161101.
10. V. Natoli and D.M. Ceperley, "An Optimized Method for Treating LongRange Potentials," J. Computational Physics, vol. 117, no. 1, 1995, pp. 171–178.
11. D.M. Ceperley and B.J. Alder, "Ground State of Solid Hydrogen at High Pressures," Physics Rev. B, vol. 36, no. 4, 1987, pp. 2092–2106.
12. M. Dewing and D.M. Ceperley, "Methods for Coupled ElectronicIonic Monte Carlo," Recent Advances in Quantum Monte Carlo Methods, World Scientific, 2002, pp. 218–253.