Accelerating Quantum Monte Carlo Simulations of Real Materials on GPU Clusters
作者： Kenneth Esler , Jeongnim Kim , David Ceperley , Luke Shulenburger
关键词: Wave function 、 GPU cluster 、 Computer science 、 Quantum computer 、 Monte Carlo method 、 CUDA 、 Quantum Monte Carlo 、 Quantum chemistry 、 Continuum mechanics 、 Computational science 、 Dynamic Monte Carlo method
摘要: More accurate than mean-field methods and more scalable quantum chemical methods, continuum Monte Carlo (QMC) is an invaluable tool for predicting the properties of matter from fundamental principles. Because QMC algorithms offer multiple forms parallelism, they're ideal candidates acceleration in many-core paradigm.
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