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

Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
Bernoulli's Equation: Problem Solving01:16

Bernoulli's Equation: Problem Solving

A Venturi meter is essential for measuring fluid flow rates in pipelines. It utilizes the relationship between fluid velocity and pressure described by Bernoulli's equation. When installed in a sewage system, the Venturi meter accurately determines the wastewater flow rate by measuring pressure differences.
The first step is to compute the cross-sectional areas of the pipe and the Venturi throat to analyze the pressure difference indicated by the pressure gauge. Next, the continuity equation is...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Application of Pascal's Law01:03

Application of Pascal's Law

Pascal's experimentally proven observations—that a change in pressure applied to an enclosed fluid is transmitted undiminished throughout the fluid and to the walls of its container—provide the foundations for hydraulics, one of the most important developments in modern mechanical technology.
Hydraulic systems are used to operate automotive brakes, hydraulic jacks, and numerous other mechanical systems. We can derive a relationship between the forces in a simple hydraulic system by applying...
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...
Bernoulli's Equation00:59

Bernoulli's Equation

In the middle of the nineteenth century, it was observed that two trains passing each other at a high relative speed get pulled towards each other. The same occurs when two cars pass each other at a high relative speed. The reason is that the fluid pressure drops in the region where the fluid speeds up. As the air between the trains or the cars increases in speed, its pressure reduces. The pressure on the outer parts of the vehicles is still the atmospheric pressure, while the resultant...

You might also read

Related Articles

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

Sort by
Same author

From the Atacama to Patagonia: Understanding the Effects of Extreme Temperatures on Birth Weight Across Climate Regions in Chile.

GeoHealth·2025
Same author

Corrigendum to "Aqueous compost extracts with stabilized biofertilizing microbiota promote plant root growth and drought resilience" [Sci. Total Environ. 974 (2025) 179157].

The Science of the total environment·2025
Same author

Cataloging olive oil mill wastewater sludge based on toxicological profiles and functional microbial diversity.

The Science of the total environment·2025
Same author

Aqueous compost extracts with stabilized biofertilizing microbiota promote plant root growth and drought resilience.

The Science of the total environment·2025
Same author

Artificial intelligence-driven analysis of embryo morphokinetics in singleton, twin, and triplet pregnancies.

Human reproduction (Oxford, England)·2025
Same author

Achievement of Target Gain Larger than Unity in an Inertial Fusion Experiment.

Physical review letters·2024
Same journal

Effective contrast-enhanced preprocessing for intracranial artery segmentation in digital subtraction angiography.

Physics in medicine and biology·2026
Same journal

Improving Plan Quality in Adaptive Proton Therapy Using an Interactive Dose Modification Tool.

Physics in medicine and biology·2026
Same journal

Technical Note: Real-Time MLC Control and Latency Measurement Optimization with External Verification.

Physics in medicine and biology·2026
Same journal

Fetus-Specific Hematopoietic Stem Cell Dosimetry Framework for Leukemia-Relevant Target Cells During Prenatal Development.

Physics in medicine and biology·2026
Same journal

Deep learning-based dose prediction to enhance planning efficiency in cervical brachytherapy with hybrid applicators.

Physics in medicine and biology·2026
Same journal

Corrigendum: Referenceless MR thermometry-a comparison of five methods (2017<i>Phys. Med. Biol</i>.<b>62</b>1-16).

Physics in medicine and biology·2026
See all related articles

Related Experiment Video

Updated: May 13, 2026

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes
11:05

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

Published on: December 13, 2016

CloudMC: a cloud computing application for Monte Carlo simulation.

H Miras1, R Jiménez, C Miras

  • 1Department of Medical Physics, Virgen Macarena Hospital, Seville, Spain. hector.miras@gmail.com

Physics in Medicine and Biology
|March 22, 2013
PubMed
Summary
This summary is machine-generated.

CloudMC enables parallel Monte Carlo simulations using cloud computing. This application achieved a 37x speedup, completing a 30-hour simulation in under an hour, demonstrating cloud benefits for dose calculations.

More Related Videos

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

Related Experiment Videos

Last Updated: May 13, 2026

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes
11:05

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

Published on: December 13, 2016

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

Area of Science:

  • Computational physics
  • Medical physics
  • Cloud computing

Background:

  • Monte Carlo simulations are crucial for accurate dose calculations in radiation therapy.
  • Parallelization is essential to reduce the long computation times associated with Monte Carlo methods.
  • Existing computational clusters have limitations in accessibility and scalability.

Purpose of the Study:

  • To present CloudMC, a cloud-based application for parallelizing Monte Carlo simulations.
  • To evaluate the performance of CloudMC on the Windows Azure platform.
  • To assess the feasibility of cloud computing for clinical Monte Carlo dose calculations.

Main Methods:

  • Developed CloudMC as a web application for dynamic virtual clusters on Windows Azure.
  • Designed CloudMC to be independent of specific Monte Carlo codes (input files → executable → output files).
  • Performed parallel Monte Carlo simulations using the Penelope code on varying numbers of Azure instances.

Main Results:

  • Instance size had no impact on simulation runtime.
  • Parallelization followed Amdahl's law, with minor deviations due to non-parallelizable components.
  • A 30-hour CPU simulation was completed in 48.6 minutes using 64 parallel instances (37x speedup).

Conclusions:

  • CloudMC effectively parallelizes Monte Carlo simulations, offering significant speedups.
  • Cloud computing provides advantages like accessibility, scalability, and pay-per-use for scientific computing.
  • Cloud-based Monte Carlo simulations hold promise for future clinical dose calculations.