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https://github.com/becksteinlab/parallel-analysis-in-the-mdanalysis-library

Benchmarking MDAnalysis with Dask (and MPI). Supplementary Information for SciPy 2017 paper.

https://github.com/becksteinlab/parallel-analysis-in-the-mdanalysis-library

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benchmark dask mdanalysis mpi trajectory
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Benchmarking MDAnalysis with Dask (and MPI). Supplementary Information for SciPy 2017 paper.

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benchmark dask mdanalysis mpi trajectory
Created almost 9 years ago · Last pushed over 8 years ago
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README.md

Parallel analysis in the MDAnalysis Library

We present a benchmark suite that can be used to evaluate performance for parallel map-reduce type analysis and use it to investigate the performance of MDAnalysis with the Dask library for task-graph based computing (Khoslessan et al, 2017).

A range of commonly used MD file formats (CHARMM/NAMD DCD, Gromacs XTC, Amber NetCDF) and different trajectory sizes are tested on different high-performance computing (HPC) resources. Benchmarks are performed both on a single node and across multiple nodes.

For space reasons, not all data could be shown in the SciPy 2017 conference proceedings paper. For a full analysis see the Technical Report (Khoshlessan and Beckstein, 2017). The report is available on figshare at DOI 10.6084/m9.figshare.4695742.

Supplementary information for SciPy 2017 paper

This repository should be considered part of the Supplementary information to the SciPy 2017 Proceedings paper (Khoslessan et al, 2017).

Benchmarking code

The repository contain the code to benchmark parallelization of MDAnalysis: * RMSD calculation with MDAnalysis with Dask for XTC, DCD, NCDF * RMSD calculation with MDAnalysis with MPI

Data files

The data files consist of a topology file adk4AKE.psf (in CHARMM PSF format; N = 3341 atoms) and a trajectory 1ake_007-nowater-core-dt240ps.dcd (DCD format) of length 1.004 μs with 4187 frames; both are freely available under the CC-BY license from figshare at DOI 10.6084/m9.figshare.5108170

Files in XTC and NetCDF formats are generated from the DCD.

Tested libraries

Comments and Questions

Please raise issues in the issue tracker or ask on the MDAnalysis developer mailing list.

References

M. Khoshlessan, I. Paraskevakos, S. Jha, and O. Beckstein (2017). Parallel analysis in MDAnalysis using the Dask parallel computing library. In S. Benthall and S. Rostrup, editors, Proceedings of the 16th Python in Science Conference, Austin, TX, 2017. SciPy.

Khoshlessan, Mahzad; Beckstein, Oliver (2017): Parallel analysis in the MDAnalysis Library: Benchmark of Trajectory File Formats. Technical report, Arizona State University, Tempe, AZ, 2017. figshare. doi:10.6084/m9.figshare.4695742

Owner

  • Name: Becksteinlab
  • Login: Becksteinlab
  • Kind: organization
  • Email: obeckste@asu.edu
  • Location: Tempe, AZ

Computational Biophysics at Arizona State University

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