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Performance Analysis of CP2K Code for Ab Initio Molecular Dynamics on CPUs and GPUs.

Dewi Yokelson1, Nikolay V Tkachenko2, Robert Robey3

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This study optimized ab initio molecular dynamics (AIMD) simulations on CPUs and GPUs. GPU acceleration significantly outperformed CPU-only runs, offering a 3.7x speedup and improving computational efficiency for catalyst system research.

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Area of Science:

  • Computational Chemistry
  • Materials Science
  • High-Performance Computing

Background:

  • Ab initio molecular dynamics (AIMD) simulations are crucial for understanding molecular catalyst systems.
  • Efficient execution of AIMD simulations requires significant computational resources.
  • Optimizing simulation performance across different hardware architectures is essential for scientific discovery.

Purpose of the Study:

  • To conduct scaling studies of AIMD simulations using the CP2K code on Intel Xeon CPU and NVIDIA V100 GPU architectures.
  • To identify optimal performance settings for both CPU and GPU-based simulations.
  • To compare the performance and scalability of CPU-only versus GPU-accelerated AIMD.

Main Methods:

  • Utilized a realistic molecular catalyst system for simulations.
  • Employed the CP2K code on Intel Xeon CPU and NVIDIA V100 GPU architectures.
  • Applied statistical methods to analyze performance variability and scaling.
  • Investigated optimal process placement and affinity settings for MPI and OpenMP.

Main Results:

  • CPU-only simulations achieved over 70% ideal scaling up to 10 compute nodes.
  • Optimal CPU performance was observed with at least four MPI ranks per node, evenly bound across sockets.
  • Fully utilizing processing cores with one OpenMP thread per core improved performance.
  • GPU-accelerated simulations on a single node with two V100 GPUs yielded a 3.7x speedup over the fastest single-node CPU-only run.
  • GPU runs demonstrated a 13% speedup compared to the fastest five-node CPU-only simulation.

Conclusions:

  • GPU acceleration offers substantial performance gains for AIMD simulations of molecular catalyst systems.
  • Careful optimization of CPU resource allocation (MPI ranks, OpenMP threads) is critical for maximizing computational efficiency.
  • Hybrid CPU-GPU approaches show significant potential for accelerating complex molecular simulations.