activity
20092025
most citedBerkeleyGW: A Massively Parallel Computer Package for the Calculation of the Quasiparticle and Optical Properties of Materials and Nanostructures

1k citations · 2.5k across the 20 of their papers we have counts for

collaborators

21 papers

cond-mat.mtrl-sci2025★ 2 cited

Advancing Quantum Many-Body GW Calculations on Exascale Supercomputing Platforms

Benran Zhang, Daniel Weinberg, Chih-En Hsu +9

Advanced ab initio materials simulations face growing challenges as increasing systems and phenomena complexity requires higher accuracy, driving up computational demands. Quantum…

cs.DC2025

GPU acceleration of non-equilibrium Green's function calculation using OpenACC and CUDA FORTRAN

Jia Yin, Khaled Z. Ibrahim, Mauro Del Ben +3

The numerical solution of the Kadanoff-Baym nonlinear integro-differential equations, which yields the non-equilibrium Green's functions (NEGFs) of quantum many-body systems, poses…

cond-mat.mtrl-sci2022★ 38 cited

Fully relativistic /Bethe-Salpeter calculations in BerkeleyGW: implementation, symmetries, benchmarking, and performance

Bradford A. Barker, Jack Deslippe, Johannes Lischner +5

Computing the quasiparticle bandstructure and Bethe-Salpeter Equation (BSE) absorption spectra for materials with spin-orbit coupling has commonly been done by treating c…

cs.DC2021

In-Situ Assessment of Device-Side Compute Work for Dynamic Load Balancing in a GPU-Accelerated PIC Code

Michael E. Rowan, Axel Huebl, Kevin N. Gott +4

Maintaining computational load balance is important to the performant behavior of codes which operate under a distributed computing model. This is especially true for GPU architect…

cs.DC2020★ 4 cited

Rapid Exploration of Optimization Strategies on Advanced Architectures using TestSNAP and LAMMPS

Rahulkumar Gayatri, Stan Moore, Evan Weinberg +5

The exascale race is at an end with the announcement of the Aurora and Frontier machines. This next generation of supercomputers utilize diverse hardware architectures to achieve t…

eess.IV2019★ 1 cited

A Molecular-MNIST Dataset for Machine Learning Study on Diffraction Imaging and Microscopy

Yan Zhang, Steve Farrell, Michael Crowley +2

An image dataset of 10 different size molecules, where each molecule has 2,000 structural variants, is generated from the 2D cross-sectional projection of Molecular Dynamics trajec…