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Simulating Lattice Spin Models on Graphics Processing Units.

Tal Levy1, Guy Cohen1, Eran Rabani1

  • 1School of Chemistry, The Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 69978, Israel.

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Accelerate lattice spin model simulations using NVIDIA GPUs and CUDA. This parallel Monte Carlo approach significantly speeds up calculations for critical phenomena and glass transitions.

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Area of Science:

  • Computational physics
  • Statistical mechanics
  • Materials science

Background:

  • Lattice spin models are crucial for understanding critical phenomena and material properties.
  • Monte Carlo (MC) simulations are standard but computationally intensive, especially near critical points.
  • Accelerating these simulations is vital for advancing research in condensed matter physics.

Purpose of the Study:

  • To develop and implement GPU-accelerated algorithms for lattice spin model simulations.
  • To enhance the efficiency of extracting equilibrium and dynamical properties.
  • To investigate performance gains using NVIDIA GPUs and CUDA.

Main Methods:

  • Development of two parallel Monte Carlo algorithms utilizing NVIDIA GPUs and CUDA.
  • Algorithm 1: Optimized for equilibrium properties near second-order phase transitions.
  • Algorithm 2: Designed for dynamical slowing down near glass transitions.

Main Results:

  • Achieved significant speedups of 70- to 150-fold compared to traditional single-threaded codes.
  • Demonstrated the effectiveness of GPU acceleration on consumer-grade hardware.
  • Validated the algorithms for both equilibrium and dynamical simulations.

Conclusions:

  • GPU acceleration with CUDA offers a powerful method to overcome computational bottlenecks in lattice spin model simulations.
  • The developed algorithms provide substantial performance improvements for studying critical phenomena and glass transitions.
  • This approach makes complex simulations more accessible and efficient for researchers.