Memory Optimization Using OVITO Pipeline

[N720720]

Description:

When analyzing molecular dynamics trajectories to calculate the Lindemann index, it's essential to manage memory efficiently, especially for large datasets. Currently, the process of loading entire trajectories into memory can be optimized by leveraging OVITO's on-demand pipeline capabilities.

Problem:

• Memory Usage can be optimized. Loading entire trajectories into memory is not always feasible, particularly for large datasets.

Solution:

By using OVITO's on-demand frame loading capabilities, we can significantly reduce memory usage. OVITO's pipeline system allows for frames to be loaded as needed, rather than all at once. This way, we can efficiently calculate the Lindemann index without excessive memory consumption.

Key Insight:

The use of Welford's online algorithm for calculating the Lindemann index is crucial in this approach. Its ability to update mean and variance incrementally allows us to take full advantage of OVITO's on-demand pipeline. This means we can process each frame individually and update our calculations without needing to store all frames in memory simultaneously.

Implementation:

The approach involves two main components:

1. Trajectory Loader: Efficiently loads trajectory frames one at a time using OVITO's pipeline, thus minimizing memory usage.
2. Lindemann Index Calculation: Computes the Lindemann index using Welford's online algorithm to update the mean and variance of distances incrementally.

Benefits:

1. Reduced Memory Usage: The on-demand loading mechanism ensures only the necessary data is in memory, making it suitable for large trajectories.
2. We can reduce the memory usage from $f * n* \frac{(n-1)}{2}$ to $n*n$, where $f$ is the number of frames and $n$ is the number of atoms.

Documentation Reference:

For more details on OVITO's data import and on-demand frame loading, refer to the OVITO documentation.

[N720720]

We can significantly reduce memory usage