Efficient implementation of the quasiparticle self-consistent GW method on GPU

2025/06/04 by Masao Obata, Takao Kotani, Obata, Masao +3 · 1 citation
Computer Science · Materials Science · #Benchmark (surveying) #CUDA #Code (set theory) #Computation #Machine Learning in Materials Science #Matrix Theory and Algorithms #Parallel Computing and Optimization Techniques #Programming style #Quasiparticle #Tensor (intrinsic definition)

paper · pdf · doi:10.48550/arxiv.2506.03477

published in arXiv (Cornell University) 260, 114190 (Cornell University)

openalex publication_date 2025/06/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

We have developed a multi-GPU version of the quasiparticle self-consistent GW (QSGW), a cutting-edge method for describing electronic excitations in a first-principles approach. While the QSGW calculation algorithm is inherently well-suited for GPU computation due to its reliance on large-scale tensor operations, achieving a maintainable and extensible implementation is not straightforward. Addressing this, we have developed a GPU version within the ecalj package, utilizing module-based programming style in modern Fortran. This design facilitates future development and code sustainability. Following the summary of the QSGW formalism, we present our GPU implementation approach and the results of benchmark calculations for two types of systems to demonstrate the capability of our GPU-supported QSGW calculations.

Citations

• Contracted Plane Wave satisfying periodic gauge
• Electronic structure and finite temperature magnetism of yttrium iron\n garnet
• Maximally localized Wannier functions: Theory and applications
• VESTA 3 for three-dimensional visualization of crystal, volumetric and morphology data
• TheGWmethod
• Generalized Gradient Approximation Made Simple

Cited by

• Machine Learning Band Gap Predictions: Linking Quasiparticle Self-Consistent GW and LDA-Derived Partial Density of States

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