Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

A CPU/MIC Collaborated Parallel Framework for GROMACS on Tianhe-2 Supercomputer.

Shaoliang Peng, Yingbo Cui, Shunyun Yang

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |June 23, 2017
    PubMed
    Summary

    This study introduces a parallel framework to accelerate GROMACS, a popular molecular dynamics software, using CPUs and Intel® Xeon Phi Many Integrated Core (MIC) coprocessors. The optimized GROMACS significantly reduces computation time for large-scale simulations.

    Related Concept Videos

    You might also read

    Related Articles

    Articles linked to this work by shared authors, journal, and citation graph.

    Sort by
    Same author

    MAGC-DTI: modality-shared space and adaptive gated interactive cross-attention for drug-target interaction prediction.

    Scientific reports·2026
    Same author

    QSyncFold: quantum neural network for multidimensional sync-discovery in protein folding.

    Briefings in bioinformatics·2026
    Same author

    Diffusion attention expert model for predicting and semi-automatic localizing STAS in lung cancer histopathological images.

    Nature communications·2026
    Same author

    CAPTAIN: a multimodal foundation model pretrained on co-assayed single-cell RNA and protein.

    Nature communications·2026
    Same author

    Multimodal pre-training models of molecular representation for drug discovery.

    National science review·2026
    Same author

    Diagnostic and interpretive gains from reasoning over conclusions with a large reasoning model in radiology.

    NPJ digital medicine·2025

    Area of Science:

    • Computational chemistry
    • High-performance computing

    Background:

    • Molecular Dynamics (MD) simulations are crucial for understanding atomic and molecular behavior.
    • GROMACS, a leading MD software, faces performance limitations with large datasets due to restricted computing resources.

    Purpose of the Study:

    • To develop and implement a CPU and Intel® Xeon Phi Many Integrated Core (MIC) coprocessor collaborated parallel framework.
    • To significantly accelerate the GROMACS software for large-scale molecular dynamics simulations.

    Main Methods:

    • Utilized an offload mode on MIC coprocessors for GROMACS acceleration.
    • Optimized GROMACS for simultaneous execution on both CPU and MIC.
    • Accelerated multi-node GROMACS for practical, large-scale applications.

    Related Experiment Videos

    Main Results:

    • Achieved significant performance improvements in GROMACS, particularly on the Tianhe-2 supercomputer.
    • Demonstrated substantial reductions in computation time through benchmarking on real data.
    • Enabled practical application of GROMACS for large-scale simulations.

    Conclusions:

    • The proposed CPU and MIC collaborated parallel framework effectively accelerates GROMACS.
    • The optimized GROMACS software offers significant computational advantages for large-scale molecular dynamics.
    • This approach enhances the feasibility of complex simulations on supercomputing platforms.