Benchmarks / Molecular & structural biology
Python Molecular & structural biology mdanalysis

Speed up MDAnalysis RMSD

MDAnalysis RMSD is one of the slower steps in many molecular & structural biology workflows. AutoZyme ships a verified, drop-in patch that is up to 4.03× faster, returning bit-for-bit identical results with no change to how you call it.

Best speedup 4.03×
Median speedup 3.56×
Output equivalence Bit-exact
Best runtime baseline 5.29 min optimized 1.31 min
Datasets 5
Pass rate 10/10

Benchmark charts

Switch benchmark platform; all charts update together
Platform
Speedup distribution
Each dot is one finalized dataset/thread run on Windows
adk_dims_concat_mediumadk_dims_concat_largeadk_dims_concat_tinyadk_perturbed_cycle_x…adk_perturbed_cycle_l…
Thread sweep
Speedup across finalized thread counts on Windows
No finalized multi-thread sweep for this platform.
Memory
Baseline vs optimized peak memory on Windows
0.0 GB5.0 GB10 GBadk_perturbed_cyc…1.00×adk_dims_concat_l…1.00×adk_perturbed_cyc…1.00×adk_dims_concat_m…1.00×adk_dims_concat_t…1.00×adk_perturbed_cycle_xlarge · ood_xlargememory 8.3 GB → 8.3 GBoptimized / baseline 1.00×3.25× speedup · 1 threadsadk_dims_concat_large · largememory 4.2 GB → 4.2 GBoptimized / baseline 1.00×3.61× speedup · 1 threadsadk_perturbed_cycle_large · ood_largememory 4.2 GB → 4.2 GBoptimized / baseline 1.00×3.23× speedup · 1 threadsadk_dims_concat_medium · mediummemory 2.1 GB → 2.1 GBoptimized / baseline 1.00×4.03× speedup · 1 threadsadk_dims_concat_tiny · smallmemory 0.8 GB → 0.8 GBoptimized / baseline 1.00×3.33× speedup · 1 threads
baselineoptimized

What is accelerated

The public API stays the same; AutoZyme replaces only the supported fast path.

This task targets MDAnalysis · RMSD in mdanalysis. The benchmarked result preserves the declared scientific output gate while reducing CPU runtime on the listed datasets.

Also searched as: RMSD, root mean square deviation, structural alignment, molecular dynamics.

Supported scope

Correct ONLY for the exact benchmark shape: RMSD over a Universe whose trajectory is a DCDReader or a ChainReader-of-DCDReaders, with NO groupselections (single select-RMSD, output is the 3-column [frame, time, rmsd] array), and run() invoked with no frame… Read full supported scope

Correct ONLY for the exact benchmark shape: RMSD over a Universe whose trajectory is a DCDReader or a ChainReader-of-DCDReaders, with NO groupselections (single select-RMSD, output is the 3-column [frame, time, rmsd] array), and run() invoked with no frame slicing (start=None, stop=None, step=None, frames=None) on the default serial backend. The fast_compute bulk path streams frames sequentially from each segment's DCDFile.readframes() and fills output column 0 with np.arange(n) and column 1 with a precomputed cumulative-time vector. This is bit-exact to upstream (pearson_r=1.0, max_abs_diff=0.0 in finalized rows) at ~3.3-4.1x speedup. The has_groups branch (groupselections present) is handled by delegating per-frame to the original _single_frame, so that case is preserved. A chunked per-frame fallback covers non-ChainReader / non-DCD readers (e.g. XTC) and is also generic-correct for the no-slicing, no-groups case.

Out-of-scope behavior

silent fallback to upstream

Show detailed speedup table 10 runs
Dataset Tier Platform Threads Baseline Optimized Speedup Memory Concordance Pass
adk_dims_concat_large large Windows 1 8.49 min 2.35 min 3.61× 4.2 → 4.2 GB — pass
adk_dims_concat_medium medium Windows 1 5.29 min 1.31 min 4.03× 2.1 → 2.1 GB — pass
adk_dims_concat_tiny small Windows 1 1.35 min 24.24 s 3.33× 0.8 → 0.8 GB — pass
adk_perturbed_cycle_large ood_large Windows 1 8.37 min 2.59 min 3.23× 4.2 → 4.2 GB — pass
adk_perturbed_cycle_xlarge ood_xlarge Windows 1 20.00 min 6.16 min 3.25× 8.3 → 8.3 GB — pass
adk_dims_concat_large large macOS 1 5.83 min 1.43 min 4.09× 7.5 → 6.6 GB — pass
adk_dims_concat_medium medium macOS 1 2.94 min 42.02 s 4.20× 4.2 → 3.7 GB — pass
adk_dims_concat_tiny small macOS 1 1.02 min 14.20 s 4.30× 1.7 → 1.4 GB — pass
adk_perturbed_cycle_large ood_large macOS 1 5.55 min 1.58 min 3.52× 7.4 → 6.7 GB — pass
adk_perturbed_cycle_xlarge ood_xlarge macOS 1 11.17 min 3.27 min 3.42× 13.4 → 11.5 GB — pass

Frequently asked questions

Speeding up MDAnalysis RMSD
Why is MDAnalysis RMSD slow?

MDAnalysis RMSD is CPU-bound, and the stock implementation in mdanalysis leaves performance on the table in its core numerical work. On the benchmark datasets the original takes 5.29 min where the AutoZyme path takes 1.31 min (4.03× faster).

How do I make MDAnalysis RMSD faster?

Install AutoZyme and activate the mdanalysis patch, then keep using MDAnalysis RMSD exactly as before. AutoZyme transparently substitutes the faster, output-validated path, up to 4.03× faster on the benchmark datasets, with no pipeline or API changes.

Does the AutoZyme speedup change the MDAnalysis RMSD output?

No. The accelerated path returns bit-for-bit identical results to the original mdanalysis implementation (maximum absolute difference 0), checked by a frozen concordance gate on every benchmark dataset.

How do I install the mdanalysis speedup?

In Python: pip install autozyme, then import autozyme and autozyme.activate("mdanalysis"). The patch applies automatically the next time you call MDAnalysis RMSD.