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Efficient compression of molecular dynamics trajectory files
Author(s) -
Marais Patrick,
Kenwood Julian,
Smith Keegan Carruthers,
Kuttel Michelle M.,
Gain James
Publication year - 2012
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.23050
Subject(s) - inter frame , computer science , lossy compression , algorithm , data compression ratio , data compression , compression ratio , quantization (signal processing) , compression (physics) , high fidelity , image compression , frame (networking) , reference frame , artificial intelligence , telecommunications , materials science , electrical engineering , composite material , internal combustion engine , automotive engineering , engineering , image (mathematics) , image processing
We investigate whether specific properties of molecular dynamics trajectory files can be exploited to achieve effective file compression. We explore two classes of lossy, quantized compression scheme: “interframe” predictors, which exploit temporal coherence between successive frames in a simulation, and more complex “intraframe” schemes, which compress each frame independently. Our interframe predictors are fast, memory‐efficient and well suited to on‐the‐fly compression of massive simulation data sets, and significantly outperform the benchmark BZip2 application. Our schemes are configurable: atomic positional accuracy can be sacrificed to achieve greater compression. For high fidelity compression, our linear interframe predictor gives the best results at very little computational cost: at moderate levels of approximation (12‐bit quantization, maximum error ≈ 10 −2 Å), we can compress a 1–2 fs trajectory file to 5–8% of its original size. For 200 fs time steps—typically used in fine grained water diffusion experiments—we can compress files to ∼25% of their input size, still substantially better than BZip2. While compression performance degrades with high levels of quantization, the simulation error is typically much greater than the associated approximation error in such cases. © 2012 Wiley Periodicals, Inc.