ArticleJournal of chemical theory and computation2024
Accelerating Pythonic Coupled-Cluster Implementations: A Comparison Between CPUs and GPUs.
Article in Journal of chemical theory and computation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Efficient Coupled-Cluster Python Frameworks for Next-Generation GPUs: A Comparative Study of CuPy and PyTorch on the Hopper and Grace Hopper Architecture.Journal of chemical theory and computation · 2026Article
- A Flexible, Automated, and Basis-Set-Insensitive Domain-Based Charge-Transfer Decomposition for Correlated Wave Functions and Its Application to Inter- and Intramolecular Cases.The journal of physical chemistry letters · 2026Article
- Electron Affinities from Equation-of-Motion Frozen Pair-Type Coupled Cluster Methods and Their Dependence on Single Excitations, Molecular Orbitals, and Basis Set Sizes.Journal of chemical theory and computation · 2025Article
- Expectation Value-pCCD-Based Methods for Single-Electron Properties.The journal of physical chemistry. A · 2025Article
- Frozen-Pair-Type pCCD-Based Methods and Their Double Ionization Variants to Predict Properties of Prototypical BN-Doped Light Emitters.Journal of chemical theory and computation · 2025Article
- Domain-Based Charge-Transfer Decomposition and Its Application to Explore the Charge-Transfer Character in Prototypical Dyes.Journal of chemical theory and computation · 2025Article
- Acceleration of the Relativistic Dirac-Kohn-Sham Method with GPU: A Pre-Exascale Implementation of BERTHA and PyBERTHA.Journal of chemical theory and computation · 2025Article
- Toward Reliable Dipole Moments without Single Excitations: The Role of Orbital Rotations and Dynamical Correlation.Journal of chemical theory and computation · 2024Article
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5 authors.
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Abstract
In this work, we benchmark several Python routines for time and memory requirements to identify the optimal choice of the tensor contraction operations available. We scrutinize how to accelerate the bottleneck tensor operations of Pythonic coupled-cluster implementations in the Cholesky linear algebra domain, utilizing a NVIDIA Tesla V100S PCIe 32GB (rev 1a) graphics processing unit (GPU). The NVIDIA compute unified device architecture API interacts with CuPy, an open-source library for Python, designed as a NumPy drop-in replacement for GPUs. Due to the limitations of video memory, the GPU calculations must be performed batch-wise. Timing results of some contractions containing large tensors are presented. The CuPy implementation leads to a factor of 10-16 speed-up of the bottleneck tensor contractions compared to computations on 36 central processing unit (CPU) cores. Finally, we compare example CCSD and pCCD-LCCSD calculations performed solely on CPUs to their CPU-GPU hybrid implementation, which leads to a speed-up of a factor of 3-4 compared to the CPU-only variant.
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