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Coupled Cluster Theory on Graphics Processing Units I. The Coupled Cluster Doubles Method.

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We developed a graphics processing unit (GPU) accelerated algorithm for the coupled cluster with double excitations (CCD) method. This GPU implementation significantly speeds up electronic structure calculations for molecules.

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Area of Science:

  • Computational Chemistry
  • Quantum Chemistry
  • Materials Science

Background:

  • Coupled cluster (CC) methods are highly accurate for describing electron correlation in molecules.
  • The coupled cluster with double excitations (CCD) equations involve matrix-matrix multiplications, suitable for parallelization.

Purpose of the Study:

  • To implement and evaluate a graphics processing unit (GPU) accelerated algorithm for the spin-free coupled cluster with double excitations (CCD) method.
  • To assess the performance gains of the GPU implementation compared to CPU-based methods and existing quantum chemistry software.

Main Methods:

  • Developed a GPU-accelerated implementation of the spin-free CCD iterative procedure.
  • Evaluated the performance on modern GPU hardware.
  • Compared computational times against multithreaded CPU algorithms and established quantum chemistry packages (Molpro, NWChem, GAMESS).
  • Investigated the use of single-precision computations for further performance enhancement.

Main Results:

  • The GPU-accelerated CCD algorithm achieved a 4-5x speedup over multithreaded CPU implementations.
  • Significant speedups were observed compared to Molpro (8-12x), NWChem (17-22x), and GAMESS (21-29x) per CC iteration.
  • Single-precision computations on the GPU doubled performance, with minimal energy errors (approx. 10^-6 hartrees).
  • Energy accuracy could be restored by a single double-precision iteration.

Conclusions:

  • GPU acceleration offers a substantial performance improvement for CCD calculations.
  • The developed algorithm provides a computationally efficient approach for electronic structure studies.
  • Single-precision computations on GPUs are viable for accelerating quantum chemistry calculations with manageable accuracy loss.