@article{b6437ede2fcc461f96f95d51ae9834b1,
title = "Accelerating quantum monte carlo simulations of real materials on GPU clusters",
abstract = "More accurate than mean-field 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 many-core paradigm.",
keywords = "Component, graphics processors, Monte Carlo, physics, scientific computing",
author = "Kenneth Esler and Jeongnim Kim and David Ceperley and Luke Shulenburger",
note = "Funding Information: This work was supported by the US Department of Energy (DOE) under contract no. DOE-DE-FG05-08OR23336 and by the US National Science Foundation under contract no. 0904572, and grants EAR-0530282 and DMS-1025392 to R.E. Cohen. Our research used resources of the US National Center for Computational Sciences and the Center for Nano-phase Materials Sciences, which are sponsored by divisions within the DOE{\textquoteright}s Advanced Scientific Computing Research and Basic Energy Sciences under contract no. DE-AC05-00OR22725. Our research was also supported by advanced computing resources provided by the US National Science Foundation, under TG-MCA93S030 and TG-MCA07S016, and used the Abe and Lincoln clusters at the US National Center for Supercomputing Applications and Kraken at the US National Institute for Computational Sciences. Thanks to R.E. Cohen at the Carnegie Institution of Washington for helpful discussions. This work was supported in part by the Carnegie Institution of Washington.",
year = "2012",
month = jan,
doi = "10.1109/MCSE.2010.122",
language = "English (US)",
volume = "14",
pages = "40--51",
journal = "Computing in Science and Engineering",
issn = "1521-9615",
publisher = "IEEE Computer Society",
number = "1",
}