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

Elastic Collisions: Case Study01:15

Elastic Collisions: Case Study

17.0K
Elastic collision of a system demands conservation of both momentum and kinetic energy. To solve problems involving one-dimensional elastic collisions between two objects, the equations for conservation of momentum and conservation of internal kinetic energy can be used. For the two objects, the sum of momentum before the collision equals the total momentum after the collision. An elastic collision conserves internal kinetic energy, and so the sum of kinetic energies before the collision equals...
17.0K
Elastic Collisions: Introduction01:00

Elastic Collisions: Introduction

12.1K
An elastic collision is one that conserves both internal kinetic energy and momentum. Internal kinetic energy is the sum of the kinetic energies of the objects in a system. Truly elastic collisions can only be achieved with subatomic particles, such as electrons striking nuclei. Macroscopic collisions can be very nearly, but not quite, elastic, as some kinetic energy is always converted into other forms of energy such as heat transfer due to friction and sound. An example of a nearly...
12.1K
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

4.5K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
4.5K
Parallel Processing01:20

Parallel Processing

950
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...
950
Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

6.2K
It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
6.2K
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

1.3K
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
1.3K

You might also read

Related Articles

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

Sort by
Same author

Efficacy of combined medication of nifedipine and magnesium sulfate on gestational hypertension and the effect on PAPP-A, VEGF, NO, Hcy and vWF.

Saudi journal of biological sciences·2020
Same author

One-Step Synthesis of Tunable Zinc-Based Nanohybrids as an Ultrasensitive DNA Signal Amplification Platform.

ACS applied materials & interfaces·2019
Same author

Polypyrrole-based double rare earth hybrid nanoparticles for multimodal imaging and photothermal therapy.

Journal of materials chemistry. B·2019
Same author

Up-regulation of plasma lncRNA CACS15 distinguished early-stage oral squamous cell carcinoma patient.

Oral diseases·2019
Same author

On the uniform stress/uniform stretch states of prestressed arteries.

Journal of theoretical biology·2019
Same author

The prognostic value of myeloid derived suppressor cell level in hepatocellular carcinoma: A systematic review and meta-analysis.

PloS one·2019

Related Experiment Video

Updated: May 3, 2026

Operation of the Collaborative Composite Manufacturing CCM System
10:09

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

6.2K

Scalable collision detection using p-partition fronts on many-core processors.

Xinyu Zhang1, Young J Kim1

  • 1Ewha Womans University, Seoul.

IEEE Transactions on Visualization and Computer Graphics
|January 18, 2014
PubMed
Summary
This summary is machine-generated.

We developed a novel parallel algorithm for efficient collision detection on multi-core processors (CPUs) and graphics processing units (GPUs). This method achieves nearly linear speedups by evenly distributing workloads without dynamic balancing, minimizing memory usage.

More Related Videos

Spatial Separation of Molecular Conformers and Clusters
10:37

Spatial Separation of Molecular Conformers and Clusters

Published on: January 9, 2014

11.0K
Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

8.7K

Related Experiment Videos

Last Updated: May 3, 2026

Operation of the Collaborative Composite Manufacturing CCM System
10:09

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

6.2K
Spatial Separation of Molecular Conformers and Clusters
10:37

Spatial Separation of Molecular Conformers and Clusters

Published on: January 9, 2014

11.0K
Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

8.7K

Area of Science:

  • Computer Science
  • Computational Geometry
  • High-Performance Computing

Background:

  • Collision detection is crucial for realistic simulations in various fields.
  • Existing parallel algorithms often suffer from high memory overhead and complex dynamic load balancing.
  • Efficiently utilizing many-core architectures (CPUs and GPUs) for collision detection remains a challenge.

Purpose of the Study:

  • To introduce a new parallel algorithm for collision detection optimized for many-core platforms.
  • To address limitations of current methods regarding workload distribution and memory overhead.
  • To demonstrate the scalability and performance of the proposed algorithm.

Main Methods:

  • A novel parallel algorithm based on the concept of a $(p)$-partition front.
  • Even workload partitioning and distribution across multiple processing cores without dynamic balancing.
  • Implementation and testing on CPU and GPU many-core computing platforms.

Main Results:

  • The algorithm achieves nearly linear performance improvement with an increasing number of processing cores.
  • Demonstrated scalability across diverse scenarios, including rigid body dynamics, cloth simulation, and random collision tests.
  • Significantly minimized memory overhead compared to state-of-the-art parallel collision detection algorithms.

Conclusions:

  • The proposed $(p)$-partition front algorithm offers an efficient and scalable solution for parallel collision detection.
  • It effectively leverages many-core architectures (CPUs and GPUs) while reducing memory footprint.
  • The method shows strong performance gains, paving the way for more complex real-time simulations.