PMDA - Parallel Molecular Dynamics Analysis. Fan, S., Linke, M., Paraskevakos, I., Gowers, R. J., Gecht, M., & Beckstein, O. In Calloway, C., Lippa, D., Niederhut, D., & Shupe, D., editors, Proceedings of the 18th Python in Science Conference, pages 134 – 142, Austin, TX, 2019.
PMDA - Parallel Molecular Dynamics Analysis [link]Paper  doi  abstract   bibtex   
MDAnalysis is an object-oriented Python library to analyze trajectories from molecular dynamics (MD) simulations in many popular formats. With the development of highly optimized MD software packages on high performance computing (HPC) resources, the size of simulation trajectories is growing up to many terabytes in size. However efficient usage of multicore architecture is a challenge for MDAnalysis, which does not yet provide a standard interface for parallel analysis. To address the challenge, we developed PMDA, a Python library that builds upon MDAnalysis to provide parallel analysis algorithms. PMDA parallelizes common analysis algorithms in MDAnalysis through a task-based approach with the Dask library. We implement a simple split-apply-combine scheme for parallel trajectory analysis. The trajectory is split into blocks, analysis is performed separately and in parallel on each block (\textbackslashtextquotedbl\\apply\textbackslashtextquotedbl\\), then results from each block are gathered and combined. PMDA allows one to perform parallel trajectory analysis with pre-defined analysis tasks. In addition, it provides a common interface that makes it easy to create user-defined parallel analysis modules. PMDA supports all schedulers in Dask, and one can run analysis in a distributed fashion on HPC machines, ad-hoc clusters, a single multi-core workstation or a laptop. We tested the performance of PMDA on single node and multiple nodes on a national supercomputer. The results show that parallelization improves the performance of trajectory analysis and, depending on the analysis task, can cut down time to solution from hours to minutes. Although still in alpha stage, it is already used on resources ranging from multi-core laptops to XSEDE supercomputers to speed up analysis of molecular dynamics trajectories. PMDA is available as open source under the GNU General Public License, version 2 and can be easily installed via the pip and conda package managers.
@inproceedings{fan_pmda_2019,
	address = {Austin, TX},
	title = {{PMDA} - {Parallel} {Molecular} {Dynamics} {Analysis}},
	url = {https://conference.scipy.org/proceedings/scipy2019/shujie_fan.html},
	doi = {10.25080/Majora-7ddc1dd1-013},
	abstract = {MDAnalysis is an object-oriented Python library to analyze trajectories from molecular dynamics (MD) simulations in many popular formats. With the development of highly optimized MD software packages on high performance computing (HPC) resources, the size of simulation trajectories is growing up to many terabytes in size. However efficient usage of multicore architecture is a challenge for MDAnalysis, which does not yet provide a standard interface for parallel analysis. To address the challenge, we developed PMDA, a Python library that builds upon MDAnalysis to provide parallel analysis algorithms. PMDA parallelizes common analysis algorithms in MDAnalysis through a task-based approach with the Dask library. We implement a simple split-apply-combine scheme for parallel trajectory analysis. The trajectory is split into blocks, analysis is performed separately and in parallel on each block ({\textbackslash}textquotedbl\{\}apply{\textbackslash}textquotedbl\{\}), then results from each block are gathered and combined. PMDA allows one to perform parallel trajectory analysis with pre-defined analysis tasks. In addition, it provides a common interface that makes it easy to create user-defined parallel analysis modules. PMDA supports all schedulers in Dask, and one can run analysis in a distributed fashion on HPC machines, ad-hoc clusters, a single multi-core workstation or a laptop. We tested the performance of PMDA on single node and multiple nodes on a national supercomputer. The results show that parallelization improves the performance of trajectory analysis and, depending on the analysis task, can cut down time to solution from hours to minutes. Although still in alpha stage, it is already used on resources ranging from multi-core laptops to XSEDE supercomputers to speed up analysis of molecular dynamics trajectories. PMDA is available as open source under the GNU General Public License, version 2 and can be easily installed via the pip and conda package managers.},
	booktitle = {Proceedings of the 18th {Python} in {Science} {Conference}},
	author = {Fan, Shujie and Linke, Max and Paraskevakos, Ioannis and Gowers, Richard J. and Gecht, Michael and Beckstein, Oliver},
	editor = {Calloway, Chris and Lippa, David and Niederhut, Dillon and Shupe, David},
	year = {2019},
	pages = {134 -- 142},
}

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