Molecular dynamics (MD) is a computational method that simulates the motions and interactions of atoms and molecules in various systems and conditions. MD can provide valuable insights into the structure, dynamics, and function of biological macromolecules, materials, and nanodevices. However, MD simulations are also very computationally demanding, requiring a large amount of CPU time and memory to achieve realistic spatial and temporal scales.

Fortunately, the development of general-purpose computing on graphics processing units (GPGPU) has opened new possibilities for accelerating MD simulations. GPUs are specialized hardware devices that can perform parallel floating-point arithmetic operations at high speed and efficiency. GPUs can be used to offload the most time-consuming parts of MD calculations, such as the evaluation of non-bonded interactions between atoms, from the CPU to the GPU. This can result in significant speedups and cost reductions for MD simulations, making them more accessible and affordable for researchers and practitioners.

There are several MD packages that have GPU support, either through the industry standard OpenCL framework or the Nvidia-specific CUDA technology. Some examples are LAMMPS, GROMACS, HOOMD, and OpenMM. These packages have different approaches and features for implementing GPU-accelerated MD algorithms, and they can achieve different levels of performance, scalability, and portability depending on the hardware and software environment. Therefore, it is important to compare and benchmark these packages to find the best solution for a given MD problem.

In this blog post, we will review some of the state-of-the-art MD packages for GPU computations, and discuss their advantages, limitations, and future directions. We will focus on generic MD systems, such as water and Lennard-Jones liquid, and explore the performance and scalability of these packages on Nvidia and AMD graphics accelerators. We will also highlight some of the challenges and opportunities for developing performance portable applications for heterogeneous parallel architectures.

LAMMPS

LAMMPS is a classical MD code that can simulate a wide range of systems and phenomena, such as soft matter, biomolecules, polymers, granular materials, and solid-state physics. LAMMPS has a modular design that allows users to customize and extend its functionality with various packages, plugins, and user-contributed codes. LAMMPS supports both OpenCL and CUDA for GPU acceleration, and it can run on single or multiple GPUs, either in a single node or in a distributed memory cluster.

LAMMPS uses a hybrid approach for GPU acceleration, where the CPU and the GPU work together to perform different parts of the MD calculation. The GPU is responsible for computing the non-bonded interactions (Van der Waals and real-space Coulomb forces), while the CPU handles the rest (PME, bonded interactions, NMR restraints, etc.). 

This approach has several benefits, such as:

•  The CPU’s power is not wasted by letting it run idle, especially since some tasks are better handled by the CPU (the GPU architecture is quite different).
• Complicated algorithms developed over the past decades don’t have to be rewritten for the GPU and are immediately available (knowledge-based force fields for protein refinement, NMR restraints, etc.).
• Macros that interact with the simulation (steered MD, etc.) work unchanged.

However, this approach also has some drawbacks, such as:

• The CPU and the GPU must have a balanced power and performance, otherwise one of them will become a bottleneck and limit the overall speedup.
• The CPU-GPU communication and synchronization can introduce significant overhead and latency, especially for large systems and multiple GPUs.
• The GPU code is not fully optimized and may not exploit all the features and capabilities of the GPU hardware.

According to the CompuBench 1.5 OpenCL benchmark, LAMMPS can achieve a speedup of about 10x for the particle simulation 64k test on a single Nvidia RTX 3090 GPU compared to a single Intel Core i9-10900K CPU¹. However, the speedup may vary depending on the system size, the force field, and the simulation settings.

OpenMM

• OpenMM is a MD code that is designed for high-performance simulation of biomolecular systems, such as proteins, DNA, and RNA.
• OpenMM has a flexible and extensible API that allows users to define custom forces, integrators, and constraints, and to integrate OpenMM with other MD packages, such as AMBER, CHARMM, and GROMACS.
• OpenMM supports both OpenCL and CUDA for GPU acceleration, and it can run on single or multiple GPUs, either in a single node or in a distributed memory cluster.

OpenMM uses a GPU-oriented approach for GPU acceleration, where the GPU performs most of the MD calculation, while the CPU acts as a coordinator and a helper. The GPU computes both the non-bonded and the bonded interactions, as well as the PME and the NMR restraints, while the CPU handles the input/output, the initialization, and the integration. 

This approach has several benefits, such as:

• The GPU is fully utilized and can achieve a high occupancy and throughput, while the CPU is minimally involved and can perform other tasks.
• The CPU-GPU communication and synchronization are minimized and optimized, reducing the overhead and latency.
• The GPU code is highly optimized and can exploit all the features and capabilities of the GPU hardware, such as shared memory, texture memory, and warp shuffle.

However, this approach also has some drawbacks, such as:

• The GPU must have enough memory and compute power to handle the entire MD calculation, otherwise the performance will degrade or the simulation will fail.
• The GPU code is more complex and less portable, and may not work well on different GPU architectures or platforms.
•  The CPU code is less flexible and extensible, and may not support some advanced features or algorithms.

According to the CompuBench 1.5 OpenCL benchmark, OpenMM can achieve a speedup of about 20x for the particle simulation 64k test on a single Nvidia RTX 3090 GPU compared to a single Intel Core i9-10900K CPU¹. However, the speedup may vary depending on the system size, the force field, and the simulation settings.

Comparison and Discussion

The table below summarizes some of the main features and differences between LAMMPS and OpenMM for GPU-accelerated MD simulations.

As we can see, LAMMPS and OpenMM have different strengths and weaknesses for GPU-accelerated MD simulations, and there is no clear winner or loser. The best choice depends on the specific MD problem and the available hardware and software resources. Some general guidelines are:

 LAMMPS is more suitable for simulating diverse and complex systems with various features and algorithms, as long as the CPU and the GPU are well balanced and the CPU-GPU communication is not too costly.

OpenMM is more suitable for simulating biomolecular systems with standard features and algorithms, as long as the GPU has enough memory and compute power and the CPU-GPU communication is not too frequent.

Conclusion

GPU-accelerated MD simulations are a powerful and promising tool for computational science, but they also pose some challenges and opportunities for future research and development. We hope that this blog post has provided you with some useful information and insights into GPU-accelerated MD simulations, and we encourage you to explore and experiment with the MD packages and the GPU devices that are available to you. Happy simulating! 😊

Source:

(1) GPU accelerated molecular dynamics – YASARA. http://www.yasara.org/gpu.htm.

(2) State-of-the-Art Molecular Dynamics Packages for GPU Computations: Performance, Scalability and Limitations – Springer. https://link.springer.com/chapter/10.1007/978-3-031-22941-1_25.

(3) CUDA Spotlight: GPU-Accelerated Molecular Dynamics – NVIDIA. https://www.nvidia.com/content/cuda/spotlights/gpu-accelerated-molecular-dynamics.html.

(4) GPU Acceleration of Molecular Modeling Applications. https://www.ks.uiuc.edu/Research/gpu/.

(5) Heterogeneous parallelization and acceleration of molecular dynamics simulations in GROMACS. https://pubs.aip.org/aip/jcp/article/153/13/134110/199476/Heterogeneous-parallelization-and-acceleration-of.