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SlaterGPU accelerates the computation of Slater-type orbital integrals using graphics processing units (GPUs) and mixed-precision, achieving over 80x speedups for electron repulsion integrals (ERIs). This enables efficient quantum chemistry calculations.

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Area of Science:

  • Computational Chemistry
  • Quantum Chemistry
  • Materials Science

Background:

  • Accurate computation of electron repulsion integrals (ERIs) is crucial for quantum chemistry.
  • Traditional methods for calculating Slater-type orbital (STO) integrals are computationally intensive.
  • Leveraging graphics processing units (GPUs) offers a pathway to accelerate these calculations.

Purpose of the Study:

  • To present SlaterGPU, a novel GPU-accelerated library for computing STO integrals.
  • To enhance the efficiency of quantum chemical calculations through optimized integral computation.
  • To demonstrate the performance and accuracy of the SlaterGPU library.

Main Methods:

  • Developed SlaterGPU using OpenACC for numerical computation of STO integrals.
  • Employed the Resolution of the Identity (RI) approximation for ERIs with Coulomb potential.
  • Utilized mixed-precision evaluation of Slater integrals to maximize GPU performance.
  • Implemented parallelization across multiple GPUs for high throughput.

Main Results:

  • Achieved over 80x speedup for ERIs using mixed-precision evaluation.
  • Reached an integral throughput exceeding 3 million integrals per second with multi-GPU parallelization.
  • Demonstrated STO integral throughput comparable to conventional Gaussian integration schemes.
  • Validated the accuracy of computed integrals through heat-bath configuration interaction (HBCI) benchmarks.

Conclusions:

  • SlaterGPU significantly accelerates the computation of STO integrals, making them accessible for large-scale quantum chemistry.
  • The library's performance is competitive with established methods, offering substantial speedups.
  • SlaterGPU effectively generates integrals required for configuration interaction calculations with large basis sets.