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GPU acceleration of hybrid functional calculations in the SPARC electronic structure code.

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We accelerated electronic structure calculations using Graphics Processing Units (GPUs). This GPU-accelerated code significantly reduces computation time and resources for hybrid functional calculations in density functional theory.

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Area of Science:

  • Computational physics
  • Materials science
  • Quantum chemistry

Background:

  • Electronic structure calculations are crucial for understanding material properties.
  • Hybrid functional calculations in density functional theory are computationally demanding.
  • Accelerating these calculations can enable larger and more complex simulations.

Purpose of the Study:

  • To develop a Graphics Processing Unit (GPU)-accelerated version of the SPARC electronic structure code.
  • To implement a batch variant of the Kronecker product-based linear solver for efficient hybrid functional calculations.
  • To optimize the code for NVIDIA GPU architectures.

Main Methods:

  • Developed a GPU-accelerated real-space SPARC code.
  • Implemented a batch variant of the Kronecker product-based linear solver.
  • Created a modular, math kernel-based implementation for hybrid functionals, offloading intensive operations to GPUs and remaining workload to CPUs.

Main Results:

  • Achieved up to 8x speedup in node-hours and 80x in core-hours compared to CPU-only execution.
  • Reduced time to solution to approximately 300 seconds for a metallic system with over 6000 electrons on V100 GPUs.
  • Significantly decreased computational resource requirements for a given wall time.

Conclusions:

  • GPU acceleration offers substantial performance gains for hybrid functional calculations in density functional theory.
  • The developed code enables faster and more resource-efficient electronic structure simulations.
  • This advancement can facilitate larger-scale materials modeling and discovery.