Path A - Module 48: Scalability & Heavy Trajectories#

What happens when your simulation trajectory is 500 GB? You cannot use msm.convert() to load it into memory. Your computer will crash.

MolSysMT was built with Big Data in mind. In this module, you will learn to use Iterators to process massive Alzheimer’s trajectories without filling your RAM.

import molsysmt as msm
from molsysmt import systems

# Even with small files, we can use the Scalability API to learn the pattern
villin_traj = systems['chicken villin HP35']['traj_chicken_villin_HP35_solvated.dcd']
villin_topo = systems['chicken villin HP35']['traj_chicken_villin_HP35_solvated.h5msm']

1. The Iterator Pattern#

An Iterator doesn’t load the data; it creates a “pointer” to the file. You can then ask for data in small chunks (e.g., 10 structures at a time).

# Create an iterator that yields 10 structures per step
iterator = msm.Iterator([villin_topo, villin_traj], selection='molecule_type=="peptide"', 
                        chunk=10, coordinates=True)

for chunk_idx, coords_chunk in enumerate(iterator):
    print(f"Processing chunk {chunk_idx}. Number of structures in this chunk: {len(coords_chunk)}")
    # Perform your analysis on coords_chunk here
    if chunk_idx == 2: break # Stop early for the demo

2. High-Level Automation: ChunkedExecutor#

Writing loops manually is prone to errors. MolSysMT provides the ChunkedExecutor which handles the iteration and the accumulation of results for you.

# Example: Calculate the average Radius of Gyration over a heavy trajectory
# (This is a simplified syntax example of the pattern)
def my_analysis(molsys):
    return msm.structure.get_radius_of_gyration(molsys, heavy_mode='off')

results = []
structure_start = 0
for coordinates in msm.Iterator(
    [villin_topo, villin_traj], chunk=20, coordinates=True
):
    structure_indices = range(structure_start, structure_start + len(coordinates))
    chunk = msm.extract(
        [villin_topo, villin_traj], structure_indices=structure_indices
    )
    results.extend(my_analysis(chunk))
    structure_start += len(coordinates)

print(f"Calculated RG for {len(results)} structures using chunked execution.")

3. Memory Efficiency#

By using these tools, your memory usage stays constant (flat line) regardless of how long the simulation is. This is the only way to process microsecond-scale simulations on a standard workstation.

🏆 Path A Challenge: The Big Data Engineer#

  1. Create an Iterator for the Villin trajectory with a chunk of 5.

  2. Inside a loop, calculate the Center of Mass of the protein for every chunk.

  3. Store only the maximum X-coordinate found in each chunk.

  4. Compare the time it takes to initialize an Iterator vs the time it takes to msm.convert() the whole file.

You can now handle any file size! In Module 49, we will learn how to benchmark the precompiled native kernels and separate loading, preparation, and execution costs.