Jove
Visualize
Contáctanos
JoVE
x logofacebook logolinkedin logoyoutube logo
ACERCA DE JoVE
Visión GeneralLiderazgoBlogCentro de Ayuda JoVE
AUTORES
Proceso de PublicaciónConsejo EditorialAlcance y PolíticasRevisión por ParesPreguntas FrecuentesEnviar
BIBLIOTECARIOS
TestimoniosSuscripcionesAccesoRecursosConsejo Asesor de BibliotecasPreguntas Frecuentes
INVESTIGACIÓN
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchivo
EDUCACIÓN
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualCentro de Recursos para ProfesoresSitio de Profesores
Términos y Condiciones de Uso
Política de Privacidad
Políticas

Videos de Conceptos Relacionados

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...
Numerical Calculations01:24

Numerical Calculations

In engineering applications, the representation of the numerical value is critical. Presenting or reporting the answer is one of the essential parts of engineering practices. Numerical calculations are performed using handheld calculators or computers since numerically accurate answers are always preferred.
The solution to a problem is obtained using different methods. While manually solving algebraic symbols is one of the most common methods, the graphical method is often preferred. Computers...
Sums of Power01:22

Sums of Power

In definite integration, Riemann sums approximate the area under a curve by dividing it into subintervals and summing the areas of rectangles. When these approximations follow predictable numerical patterns, such as arithmetic or polynomial sequences, sum formulas offer a more efficient and accurate way to compute the result. In particular, the sum of consecutive integers, squares, and cubes plays an essential role in simplifying these calculations, especially when dealing with uniform...
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of the problem,...
Parallel-axis Theorem01:06

Parallel-axis Theorem

The parallel-axis theorem provides a convenient and quick method of finding the moment of inertia of an object about an axis parallel to the axis passing through its center of mass. Consider a thin rod as an example. There is a striking similarity between the process of finding the moment of inertia of a thin rod about an axis through its middle, where the center of mass lies, and about an axis through its end using the conventional method. In the conventional method, the concept of linear mass...
Area Computation by the Alternative Coordinate Method01:24

Area Computation by the Alternative Coordinate Method

The alternative coordinate method, also known as the Shoelace Formula, is a technique for determining the area of a traverse using Cartesian coordinates. This method relies on the sequential arrangement of x and y coordinates for each point of the shape, ensuring accuracy and ease of application.In this approach, each corner's x and y coordinates are listed as fractions, with the x-coordinate as the numerator and the y-coordinate as the denominator. These coordinates are arranged sequentially...

También podría leer

Artículos Relacionados

Artículos vinculados a este trabajo por autores compartidos, revista y gráfico de citas.

Ordenar por
Same author

Simulation of a generalized asset exchange model with economic growth and wealth distribution.

Physical review. E·2021
Same author

An outbreak of infectious hepatitis among chimpanzee handlers at a United States Air Force Base.

Project report. USAF School of Aviation Medicine·2014
Same author

Fast lattice Boltzmann solver for relativistic hydrodynamics.

Physical review letters·2010
Same author

Generalization of Metropolis and heat-bath sampling for Monte Carlo simulations.

Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics·2002
Same author

Fourier acceleration of Langevin molecular dynamics.

Physical review. E, Statistical, nonlinear, and soft matter physics·2001
Same author

Three-dimensional hydrodynamic lattice-gas simulations of domain growth and self-assembly in binary immiscible and ternary amphiphilic fluids.

Physical review. E, Statistical, nonlinear, and soft matter physics·2001

Video Experimental Relacionado

Updated: Jul 12, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

La computación científica paralela paralela es una computación científica paralela.

W D Hillis, B M Boghosian

    Science (New York, N.Y.)
    |August 13, 1993
    PubMed
    Resumen

    Las computadoras masivas paralelas proporcionan nuevas capacidades de computación científica. Este artículo examina las aplicaciones de computación paralela y los desafíos de programación para los científicos que usan estas potentes máquinas.

    Área de la Ciencia:

    • Ciencias computacionales Ciencias computacionales.
    • Computación de alto rendimiento de alto rendimiento.

    Sus antecedentes:

    • Las computadoras serie tradicionales tienen limitaciones para los cálculos científicos a gran escala.
    • Las computadoras masivas paralelas ofrecen una mayor potencia computacional, pero requieren diferentes enfoques.

    Objetivo del estudio:

    • Estudiar la aplicabilidad de los ordenadores paralelos para los cálculos científicos.
    • Para resumir la comprensión actual de las cuestiones de programación para aplicaciones científicas paralelas.

    Principales métodos:

    • Estudio de una serie de ejemplos de computación científica en ordenadores paralelos.
    • Analizando las diferencias entre algoritmos paralelos y secuenciales.

    Principales resultados:

    Videos de Experimentos Relacionados

    Last Updated: Jul 12, 2026

    Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
    05:30

    Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

    Published on: September 8, 2023

    • La mayoría de los cálculos científicos a gran escala son susceptibles a la computación paralela.
    • Los algoritmos paralelos a menudo difieren significativamente de los algoritmos secuenciales.

    Conclusiones:

    • La computación paralela es una herramienta poderosa para el avance científico.
    • Comprender la programación paralela es crucial para aprovechar estos sistemas de manera efectiva.