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.
Kenneth Esler, Jeongnim Kim, David Ceperley and Luke Shulenburger. Accelerating Quantum Monte Carlo Simulations of Real Materials on GPU Clusters. Computing in Science and Engineering. Volume 14 issue 1, pages 40-51, 2012. [doi: 10.1109/MCSE.2010.122]