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Markus Schmitt
The Diagrammatic Determinantal Quantum Monte Carlo (DDQMC) algorithm [11, s. III] is used to solve quantum impurity models such as the Anderson model [13]. The topic of this dissertation is the efficient porting of an existing implementation of DDQMC to CUDA in order to use GPUs as accelerators. The main characteristics of quantum impurity models […]
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Yutaka Uejima, Ryo Maezono
We accelerated an {it ab-initio} QMC electronic structure calculation by using GPGPU. The bottleneck of the calculation for extended solid systems is replaced by CUDA-GPGPU subroutine kernels which build up spline basis set expansions of electronic orbital functions at each Monte Carlo step. We achieved 30.8 times faster evaluation for the bottleneck, confirmed on the […]
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Andres Tomas, Chia-Chen Chang, Richard Scalettar, Zhaojun Bai
The Determinant Quantum Monte Carlo (DQMC) method is one of the most powerful approaches for understanding properties of an important class of materials with strongly interacting electrons, including magnets and superconductors. It treats these interactions exactly, but the solution of a system of N electrons must be extrapolated to bulk values. Currently N ~ 500 […]
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Shanthan Mudhasan
Any system in the world constitutes particles like electrons. To analyze the behaviors of these systems the behavior of these particles must be predicted. The ground state energy of a molecule is the most important information about a molecule and can calculate by solving the Schrodinger equation. But as the number of atoms increase, the […]
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Akila Gothandaraman
Scientific computing applications demand ever-increasing performance while traditional microprocessor architectures face limits. Recent technological advances have led to a number of emerging computing platforms that provide one or more of the following over their predecessors: increased energy efficiency, programmability/flexibility, different granularities of parallelism, and higher numerical precision support. This dissertation explores emerging platforms such as […]
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Akila Gothandaraman, Rick Weber, Gregory D. Peterson, Robert J. Hinde, Robert J. Harrison
Recent technological advances have led to a number of emerging platforms such as multi-cores, reconfigurable computing, and graphics processing units. We present a comparative study of multi-cores, field-programmable gate arrays, and graphics processing units for a Quantum Monte Carlo chemistry application. The speedups of these implementations are measured relative to a multi-core implementation and the […]
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Kenneth P. Esler, Jeongnim Kim, David M. Ceperley
Continuum quantum Monte Carlo (QMC) has proved to be an invaluable tool for predicting the properties of matter from fundamental principles. By solving the manybody Schrodinger equation through a stochastic projection, it achieves greater accuracy than mean-field methods and better scalability than quantum chemical methods, enabling scientific discovery across a broad spectrum of disciplines. The […]
Tomoharu Terashima, Ryo Maezono
We accelerated an ab-initio molecular QMC calculation by using GPGPU. Only the bottle-neck part of the calculation is replaced by CUDA subroutine and performed on GPU, getting 23.5 (9.7) times faster performance in single (double) precision. The energy deviation caused by the single precision treatment was found to be within the accuracy required in the […]
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Free GPU computing nodes at hgpu.org

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