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https://github.com/becksteinlab/scipy2021-mpih5md-data


https://github.com/becksteinlab/scipy2021-mpih5md-data

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# Scipy2021: MPI-parallel Molecular Dynamics Trajectory Analysis with the H5MD Format in the MDAnalysis Python Package

[![DOI](https://zenodo.org/badge/339555913.svg)](https://zenodo.org/badge/latestdoi/339555913)



Data and supplementary information for the paper



* Edis Jakupovic and Oliver Beckstein, “[MPI-parallel Molecular Dynamics Trajectory Analysis with the H5MD Format in the MDAnalysis Python Package](http://conference.scipy.org/proceedings/scipy2021/edis_jakupovic.html),” in Proceedings of the 20th Python in Science Con- ference (Meghann Agarwal, Chris Calloway, Dillon Niederhut, and David Shupe, eds.), pp. 18 – 26, 2021. DOI: To be added.



Short Summary

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MDAnalysis is a Python library for the analysis of molecular dynamics simulations. As datafiles are now typically terabytes in size, file I/O has become a bottleneck in the workflow of analyzing simulation trajectories. We have implemented an HDF5-based file format trajectory reader into MDAnalysis that can perform parallel MPI IO and benchmarked it on various national HPC environments. Although speed-ups on the order of 20x for 48 cores are attainable, scaling up to achieve higher parallel data ingestion rates remains challenging. We developed several algorithmic optimizations in our analysis workflows that lead to improvements in IO times of up to 98x on 112 cores with respect to baseline performance.



Abstract

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Molecular dynamics (MD) computer simulations help elucidate details of the molecular processes in complex biological systems, from protein dynamics to drug discovery.

One major issue is that these MD simulation files are now commonly terabytes in size, which means analyzing the data from these files becomes a painstakingly expensive task.

In the age of national supercomputers, methods of parallel analysis are becoming a necessity for the efficient use of time and high performance computing (HPC) resources but for any approach to parallel analysis, simply reading the file from disk becomes the performance bottleneck that limits overall analysis speed.

One promising way around this file I/O hurdle is to use a parallel message passing interface (MPI) implementation with the HDF5 (Hierarchical Data Format 5) file format to access a single file simultaneously with numerous processes on a parallel file system.

Our previous feasibility study [Khoshlessan2020] suggested that this combination can lead to favorable parallel scaling with hundreds of CPU cores, so we implemented a fast and user-friendly HDF5 reader (the ``H5MDReader`` class) that adheres to H5MD (HDF5 for Molecular Dynamics) specifications [Buyl2013].

We made ``H5MDReader`` (together with a H5MD output class ``H5MDWriter``) available in the MDAnalysis library, a Python package that simplifies the process of reading and writing various popular MD file formats by providing a streamlined user-interface that is independent of any specific file format [Gowers2016].

We benchmarked H5MDReader's parallel file reading capabilities on three HPC clusters: ASU Agave, SDSC Comet, and PSC Bridges.

The benchmark consisted of a simple split-apply-combine scheme of an I/O bound task that split a 90k frame (113 GiB) coordinate trajectory into :math:`N` chunks for :math:`N` processes, where each process performed the commonly used RMSD (root mean square distance after optimal structural superposition) calculation on their chunk of data, and then gathered the results back to the root process.

For baseline performance, we found maximum I/O speedups at 2 full nodes, with Agave showing 20x, and a maximum computation speedup on Comet of 373x on 384 cores (all three HPCs scaled well in their computation task).

We went on to test a series of optimizations attempting to speed up I/O performance, including adjusting file system stripe count, implementing a masked array feature that only loads relevant data for the computation task, front loading all I/O by loading the entire trajectory into memory, and manually adjusting the HDF5 dataset chunk shapes.

We found the largest improvement in I/O performance by optimizing the chunk shape of the HDF5 datasets to match the iterative access pattern of our analysis benchmark.

With respect to baseline serial performance, our best result was a 98x speedup at 112 cores on ASU Agave.

In terms of absolute time saved, the analysis went from 4623 seconds in the baseline serial run to 47 seconds in the parallel, properly chunked run.

Our results emphasize the fact that file I/O is not just dependent on the access pattern of the file, but more so the synergy between access pattern and the layout of the file on disk.



Keywords

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HDF5, H5MD, MPI I/O, MDAnalysis, High Performance Computing



References

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[Buyl2013] P. Buyl, P. Colberg, and Felix Höfling. "H5MD: A structured, efficient, and portable file format for molecular data," Computer Physics Communications, 185, 2013 doi:10.1016/j.cpc.2014.01.018.



[Gowers2016] R. J. Gowers, M. Linke, J. Barnoud, T. J. E. Reddy, M. N. Melo, S. L. Seyler, D. L. Dotson, J. Domański, S. Buchoux, I. M. Kenney, and O. Beckstein, “MDAnalysis: A Python package for the rapid analysis of molecular dynamics simulations,” in Proceedings of the 15th Python in Science Conference (S. Benthall and S. Rostrup, eds.), (Austin, TX), pp. 98–105, SciPy, 2016. doi:10.25080/Majora-629e541a-00e



[Khoshlessan2020] M. Khoshlessan, I. Paraskevakos, G. C. Fox, S. Jha, and O. Beckstein, “Parallel performance of molecular dynamics trajectory analysis,” Concurrency and Computation: Practice and Experience, vol. 32, p. e5789, 2020. doi:10.1002/cpe.5789