GANSU: A GPU-Native Quantum Chemistry Framework for Efficient Hartree–Fock and Post-HF Calculations
Yasuaki Ito, Satoki Tsuji, Koji Nakano +1 more
GPU-accelerated quantum chemistry programs can dramatically reduce the time required for electronic structure calculations, yet most existing implementations either retrofit GPU kernels onto legacy CPU codebases or optimize individual kernels without addressing workflow-level integration overhead. We present GANSU (GPU Accelerated Numerical Simulation Utility), an open-source quantum chemistry framework written entirely in CUDA/C++ that integrates GPU-accelerated kernels for electron repulsion integrals, Fock matrix construction, and post-Hartree–Fock (post-HF) methods into a unified, GPU-resident execution pipeline. The key design principle is to eliminate host–device data transfers between computational stages by keeping all intermediate data, including density matrices, integral buffers, and Fock matrix replicas, on the GPU throughout the self-consistent field (SCF) iteration, combined with runtime-selectable integral strategies (stored ERI, resolution-of-the-identity, and Direct-SCF) that adapt to system size and available memory. On an NVIDIA H200 GPU, GANSU achieves end-to-end speedups of up to 52× over PySCF for SCF, 45× for MP2 on molecules with up to 470 basis functions, and 44× for FCI, while outperforming GPU4PySCF by up to 34× for FCI, across a range of molecular systems with up to 650 basis functions. The framework further provides analytical energy gradients and geometry optimization with nine algorithms, all operating within the same GPU-resident data flow. These results demonstrate that workflow-aware kernel integration, not just kernel-level optimization, is essential for realizing the full potential of GPU acceleration in scientific computing. GANSU is publicly available under the BSD-3-Clause license.
Eng—Advances in Engineering•Apr 28, 2026•1 citation