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Large-Scale Condensed Matter DFT Simulations: Performance and Capabilities of the CRYSTAL Code.

A Erba1, J Baima1, I Bush2

  • 1Dipartimento di Chimica, Università di Torino , Via Giuria 5, 10125 Torino, Italy.

Journal of Chemical Theory and Computation
|September 6, 2017
PubMed
Summary
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This study enhances the Crystal17 package for efficient, large-scale first-principles calculations on solids using high-performance computing. The improved parallelization enables accurate simulations of complex materials with thousands of atoms.

Area of Science:

  • Computational Materials Science
  • Quantum Mechanics
  • Solid-State Physics

Background:

  • Efficient high-performance computing (HPC) is vital for first-principles calculations of large systems.
  • Parallelization strategies are key to managing computational complexity in quantum-mechanical algorithms.
  • Extending parallelization to all properties, not just basic functions, is challenging but necessary.

Purpose of the Study:

  • To discuss the performance and capabilities of the massively parallel Crystal17 package.
  • To present recent developments enhancing code scalability for HPC.
  • To analyze the computational demands for large-scale solid-state calculations.

Main Methods:

  • Implementation and testing of advanced parallelization strategies within the Crystal17 package.

Related Experiment Videos

  • Quantitative analysis of code scalability and memory usage on HPC clusters.
  • Validation of numerical size consistency for large atomic systems.
  • Application to ab initio studies of diverse physical properties.

    • Main Results:

    • Demonstrated significant improvements in Crystal17 code scalability, supporting up to 32,768 cores.
    • Quantified scaling and memory requirements for calculations involving up to 14,000 atoms per cell.
    • Documented high numerical size consistency, ensuring accuracy.
    • Showcased successful ab initio investigations of various material properties.

    Conclusions:

    • The enhanced Crystal17 package effectively leverages HPC resources for large-scale first-principles solid-state calculations.
    • The code demonstrates excellent scalability and accuracy for complex systems.
    • This advancement facilitates deeper understanding of material properties through advanced computational modeling.