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Area of Science:

  • Computational Chemistry
  • Quantum Chemistry
  • High-Performance Computing

Background:

  • Efficient computation of two-electron integrals is crucial for Hartree-Fock and density functional theory (DFT) methods.
  • Parallelization strategies are essential for tackling the computational demands of modern electronic structure calculations.
  • Heterogeneous computing environments, combining CPUs and GPUs, offer potential for performance gains but require specialized algorithms.

Purpose of the Study:

  • To develop a parallel integral algorithm for two-electron contributions in Hartree-Fock and hybrid DFT.
  • To achieve strong scaling parallelization on inhomogeneous compute clusters, including hybrid CPU/GPU systems.
  • To investigate the efficient utilization of both CPUs and GPUs, particularly for large basis sets on GPUs.

Main Methods:

  • A novel parallel integral algorithm for two-electron contributions was developed.
  • The algorithm was designed for strong scaling on inhomogeneous compute clusters.
  • Strategies for simultaneous and efficient use of CPUs and graphics processing units (GPUs) were implemented.
  • A general approach for using large basis sets (e.g., quadruple-ζ split valence) on GPUs was established.

Main Results:

  • The parallel algorithm demonstrates strong scaling performance on both hybrid CPU/GPU and pure CPU environments.
  • Efficient simultaneous execution on CPUs and GPUs was achieved, despite architectural differences.
  • The study investigated the performance balance between CPUs and GPUs based on l-quantum numbers.
  • Successful application of large basis sets on GPUs was demonstrated.

Conclusions:

  • The presented parallel integral algorithm enables efficient, strong-scaling computations for Hartree-Fock and hybrid DFT.
  • The approach effectively leverages heterogeneous computing resources (CPUs and GPUs) for quantum chemistry.
  • The strategy facilitates the use of large basis sets on GPUs, advancing computational capabilities.