Question related to large data

[ishasinghrathore37]

MDAnalysis supports large trajectory files from multiple simulation engines. Are there ongoing efforts to improve memory efficiency or streaming-based trajectory processing for very large datasets?

[orbeckst]

I think you need to be more specific about the problems that you're asking about.

Under which circumstances do you think memory is an issue?

What do you consider "very large datasets" and how do you expect them being processed? Where do you think bottlenecks are that need addressing?

[ishasinghrathore37]

Thank you for the clarification and for the guidance!

By “very large datasets,” I am referring to trajectories on the order of tens of gigabytes (e.g., 10–100 GB), systems with ~10^5–10^6 atoms, or very long trajectories with 10^4–10^5 frames. In such cases, loading the entire trajectory into memory can exceed typical workstation RAM and becomes impractical.

I was curious about two potential bottlenecks:

1. Memory usage during frame loading when full trajectories are kept in memory rather than streamed.
2. Computational cost of operations like pairwise distances or selections for large atom groups, which can create large temporary NumPy arrays.

Specifically, I was wondering:

• Is memory usage for trajectory handling currently a known limitation for such large-scale datasets?
• Are there plans or ongoing efforts to support more efficient streaming or chunk-based processing workflows to avoid loading entire trajectories?
• From your experience, which parts (I/O vs. in-memory analysis vs. backend execution) tend to be the most costly in practice for large systems?

Thank you again for the guidance — any pointers on where performance limitations lie would be very helpful!

[orbeckst]

At the moment there's not a mechanism to partially load trajectories larger than the RAM or to buffer/do some async IO. The reason why MDA defaults to loading only a single frame at the time is to be able to handle large files.

Do you have evidence for large temp arrays in the situations that you mention?

Memory usage is not an issue unless you want to read large trajectories.

The actual I/O is typically the slowest part. https://proceedings.scipy.org/articles/majora-1b6fd038-005 has some data on costs.

[ishasinghrathore37]

Thank you for the detailed explanation and the reference!

That makes sense — if MDAnalysis already loads only a single frame at a time, then memory pressure from trajectory loading itself would indeed be limited. I also appreciate the clarification that I/O tends to be the main bottleneck.

Regarding the temporary arrays I mentioned, I do not currently have concrete benchmarks demonstrating memory issues in those cases. I was mainly thinking about analyses such as pairwise distance calculations on large atom groups, where intermediate NumPy arrays could potentially become large.

I will take a closer look at the benchmarks and the cost data you shared and try to better understand where the actual performance bottlenecks occur in practice. If I find concrete examples where memory usage or temporary arrays become problematic, I will share them here.

Thanks again for the guidance — it is very helpful for understanding the design choices in MDAnalysis.