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On the Efficient Evaluation of the Exchange Correlation Potential on Graphics Processing Unit Clusters.

David B Williams-Young1, Wibe A de Jong1, Hubertus J J van Dam2

  • 1Lawrence Berkeley National Laboratory, Computational Research Division, Berkeley, CA, United States.

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|December 28, 2020
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Summary

This study introduces a new parallel computing method for Kohn-Sham density functional theory (KS-DFT) on graphics processing units (GPUs). The approach enhances computational efficiency for large-scale molecular and materials science simulations.

Keywords:
density functional theorygraphics processing unithigh-performance computingparallel computingquantum chemistry

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Area of Science:

  • Computational Chemistry
  • Materials Science
  • High-Performance Computing (HPC)

Background:

  • Kohn-Sham density functional theory (KS-DFT) is crucial for simulating large molecular and materials systems.
  • High-performance computing (HPC) advancements, particularly graphics processing units (GPUs), are essential for maintaining computational efficiency.
  • Existing KS-DFT software requires adaptation to heterogeneous computing architectures for optimal performance.

Purpose of the Study:

  • To develop and implement a novel three-level parallelism scheme for KS-DFT calculations on GPU-accelerated computing clusters.
  • To leverage batched kernels, including batched level-3 BLAS, for enhanced GPU performance in exchange-correlation (XC) potential integration.
  • To demonstrate the performance and scalability of the new implementation against existing CPU-based methods.

Main Methods:

  • A three-level parallelism strategy was designed for distributed numerical integration of the XC potential.
  • The Gaussian basis set discretization of Kohn-Sham equations was employed on compute nodes with multiple GPUs.
  • Batched kernels, specifically batched level-3 BLAS operations, were utilized to optimize GPU performance.

Main Results:

  • The purposed three-level parallelism scheme and batched kernels significantly improve performance on GPU architectures.
  • The NWChemEx implementation demonstrates high scalability for large-scale KS-DFT computations.
  • Performance comparisons show advantages over existing scalable CPU-based exchange-correlation integration methods.

Conclusions:

  • The developed method effectively utilizes heterogeneous HPC architectures for efficient KS-DFT calculations.
  • This work provides a pathway for accelerating large-scale simulations in chemistry and materials science.
  • The implementation in NWChemEx offers a scalable and high-performance solution for modern computational challenges.