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Accelerating Quantum Monte Carlo Simulations of Real Materials on GPU Clusters

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4 Author(s)
Esler, K.P. ; Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA ; Jeongnim Kim ; Ceperley, D.M. ; Shulenburger, L.

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.

Published in:

Computing in Science & Engineering  (Volume:14 ,  Issue: 1 )