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

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...
Vector Algebra: Method of Components01:08

Vector Algebra: Method of Components

It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
In many applications, the magnitudes and directions of...
A Single-Component System01:24

A Single-Component System

In the field of chemistry, the terms "component" and "phase" hold significant importance. A component refers to a chemically distinct substance in a system that has specific properties. It is chemically homogeneous, meaning it has the same properties throughout. For example, in a mixture of salt and water, both salt and water are considered separate components because they have different chemical properties.On the other hand, a phase is a form of matter that has a consistent chemical...
Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
Normal and Tangetial Components: Problem Solving01:24

Normal and Tangetial Components: Problem Solving

Consider a man with a mass of 70 kg seated in a chair connected to a pin support through a member BC. If the man maintains an upright position, the task is to determine the horizontal and vertical reactions of the chair on the man when the member makes a 45° angle with the horizontal. At this moment, the man has a speed of 5 m/s, increasing at a rate of 1 m/s².
Block Diagram Reduction01:22

Block Diagram Reduction

The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
The first step in this process is the identification and relocation of a branch point. A branch point, where a...

You might also read

Related Articles

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

Sort by
Same author

Cluster percolation and dynamical scaling in the Baxter-Wu model.

Physical review. E·2026
Same author

Partition Function Zeros of the Frustrated <i>J</i><sub>1</sub>-<i>J</i><sub>2</sub> Ising Model on the Honeycomb Lattice.

Entropy (Basel, Switzerland)·2024
Same author

Universal Fragility of Spin Glass Ground States under Single Bond Changes.

Physical review letters·2024
Same author

Resampling schemes in population annealing: Numerical and theoretical results.

Physical review. E·2024
Same author

Geometric clusters in the overlap of the Ising model.

Physical review. E·2023
Same author

Weighted averages in population annealing: Analysis and general framework.

Physical review. E·2022
Same journal

Tension on dsDNA bound to ssDNA-RecA filaments may play an important role in driving efficient and accurate homology recognition and strand exchange.

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Amplitude-phase coupling drives chimera states in globally coupled laser networks [Phys. Rev. E 91, 040901(R) (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Erratum: Shapes of sedimenting soft elastic capsules in a viscous fluid [Phys. Rev. E 92, 033003 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Erratum: Attenuation of excitation decay rate due to collective effect [Phys. Rev. E 90, 022142 (2014)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Role of connectivity and fluctuations in the nucleation of calcium waves in cardiac cells [Phys. Rev. E 92, 052715 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Lattice Boltzmann approach for complex nonequilibrium flows [Phys. Rev. E 92, 043308 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
See all related articles

Related Experiment Video

Updated: May 27, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Connected-component identification and cluster update on graphics processing units.

Martin Weigel1

  • 1Institut für Physik, Johannes Gutenberg-Universität Mainz, Staudinger Weg 7, D-55099 Mainz, Germany. weigel@uni-mainz.de

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|November 9, 2011
PubMed
Summary
This summary is machine-generated.

Graphics Processing Units (GPUs) offer significant speedups for local physics simulations. However, nonlocal cluster identification tasks present challenges for GPU parallelization, requiring careful algorithm selection and comparison to serial implementations.

Related Experiment Videos

Last Updated: May 27, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Area of Science:

  • Physics and Engineering
  • Computational Science

Background:

  • Cluster identification is crucial in diverse fields like spin models, percolation simulations, image processing, and network analysis.
  • Graphics Processing Units (GPUs) excel at parallelizing local computations, achieving significant speedups over Central Processing Units (CPUs).
  • Nonlocal problems, such as cluster identification, pose greater parallelization challenges for GPUs compared to local tasks.

Purpose of the Study:

  • To evaluate the effectiveness of various parallelization strategies for cluster labeling and update algorithms on GPUs.
  • To compare the performance of GPU-accelerated cluster identification methods against traditional serial implementations.

Main Methods:

  • Exploration of different parallelization approaches for cluster identification algorithms suitable for GPU architecture.
  • Performance benchmarking of GPU implementations against serial algorithms on CPUs.

Main Results:

  • While local tasks see substantial GPU speedups, nonlocal cluster identification presents complexities.
  • The suitability of different parallelization techniques for GPU-based cluster identification varies.

Conclusions:

  • Optimizing GPU performance for nonlocal cluster identification requires careful consideration of parallelization strategies.
  • Comparative analysis is essential to determine the most efficient approach for GPU cluster identification tasks.