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 Concept Videos

Parallel Processing01:20

Parallel Processing

190
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
190
Improving Translational Accuracy02:07

Improving Translational Accuracy

11.7K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.7K
Survival Tree01:19

Survival Tree

126
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
126

You might also read

Related Articles

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

Sort by
Same author

Plasma Proteomics Analysis of Early Biomarkers for Predicting Female Fecundability: A Nested Case-Control Study.

Journal of proteome research·2024
Same author

Serum 5'-Nucleotidase as a Novel Predictor of Adverse Clinical Outcomes after Percutaneous Coronary Intervention in Patients with Coronary Artery Disease.

Reviews in cardiovascular medicine·2024
Same author

Evidence of a distinct collective mode in Kagome superconductors.

Nature communications·2024
Same author

Effect of lumbosacral transitional vertebra on developmental alterations of the hip: a quantitative investigation of the lumbo-pelvic-hip complex via whole-body computed tomography.

Quantitative imaging in medicine and surgery·2024
Same author

Structures of SenB and SenA enzymes from <i>Variovorax paradoxus</i> provide insights into carbon-selenium bond formation in selenoneine biosynthesis.

Heliyon·2024
Same author

TMPRSS2 and glycan receptors synergistically facilitate coronavirus entry.

Cell·2024
Same journal

Blue Noise Dithering for Reservoir-based Spatio-temporal Importance Resampling.

IEEE transactions on visualization and computer graphics·2026
Same journal

ROS-GS: Relightable Outdoor Scenes With Gaussian Splatting.

IEEE transactions on visualization and computer graphics·2026
Same journal

MesoSplats: Texture Synthesis with Gaussian Splatting.

IEEE transactions on visualization and computer graphics·2026
Same journal

GLLA: A Unified Force-Directed Graph Layout Framework Supporting Local Adjustments.

IEEE transactions on visualization and computer graphics·2026
Same journal

Multi-Perception Crowd: Learning to combine entity and implicit perception for diverse crowd simulation.

IEEE transactions on visualization and computer graphics·2026
Same journal

Hiding in Plain Sight: Camouflaging Real-world Objects.

IEEE transactions on visualization and computer graphics·2026
See all related articles

Related Experiment Video

Updated: Aug 4, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.3K

Visual Diagnostics of Parallel Performance in Training Large-Scale DNN Models.

Yating Wei, Zhiyong Wang, Zhongwei Wang

    IEEE Transactions on Visualization and Computer Graphics
    |April 6, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a visual analytics approach for diagnosing deep neural network (DNN) training performance issues in large clusters. It helps identify the root causes of inefficiencies in parallel training processes.

    More Related Videos

    A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
    09:34

    A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

    Published on: September 25, 2021

    4.0K
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    470

    Related Experiment Videos

    Last Updated: Aug 4, 2025

    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    9.3K
    A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
    09:34

    A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

    Published on: September 25, 2021

    4.0K
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    470

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Data Visualization

    Background:

    • Diagnosing large-scale deep neural network (DNN) training performance in clusters is crucial but challenging due to complex parallelization and data volume.
    • Existing methods analyzing individual device performance lack root cause analysis capabilities for parallel training anomalies.

    Purpose of the Study:

    • To develop a visual analytics approach for interactive exploration and root cause diagnosis of DNN parallel training performance issues.
    • To empower analysts in understanding and optimizing the efficiency of large-scale DNN model training.

    Main Methods:

    • Gathered design requirements through domain expert discussions.
    • Proposed an enhanced execution flow for illustrating parallelization strategies within computational graphs.
    • Designed and implemented an enhanced Marey's graph with time-span and banded visuals for training dynamics.
    • Developed a visual aggregation technique to enhance visualization efficiency.

    Main Results:

    • Demonstrated the approach's effectiveness through case studies on PanGu-α 13B and Resnet models.
    • Validated the approach via a user study and expert interviews, confirming its utility in identifying training inefficiencies.
    • The enhanced Marey's graph effectively conveys training dynamics and aids in pinpointing performance bottlenecks.

    Conclusions:

    • The proposed visual analytics approach enables effective interactive diagnosis of DNN parallel training performance issues.
    • This method significantly improves the ability to identify and address root causes of inefficiencies in large-scale distributed training.
    • The visual enhancements provide deeper insights into training dynamics, facilitating optimization efforts.