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Accelerating Fluids01:17

Accelerating Fluids

When a fluid is in constant acceleration, the pressure and buoyant force equations are modified. Suppose a beaker is placed in an elevator accelerating upward with a constant acceleration, a. In the beaker, assume there is a thin cylinder of height h with an infinitesimal cross-sectional area, ΔS.
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Acceleration Vectors01:30

Acceleration Vectors

In everyday conversation, accelerating means speeding up. Acceleration is a vector in the same direction as the change in velocity, Δv, therefore the greater the acceleration, the greater the change in velocity over a given time. Since velocity is a vector, it can change in magnitude, direction, or both. Thus acceleration is a change in speed or direction, or both. For example, if a runner traveling at 10 km/h due east slows to a stop, reverses direction, and continues their run at 10 km/h due...
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Methods of Medium Optimization

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Turbulent Flow01:24

Turbulent Flow

Turbulent flow is characterized by unpredictable fluctuations in velocity and pressure, which result in a chaotic fluid movement distinct from the orderly patterns of laminar flow. While laminar flow is governed by smooth, parallel layers with minimal mixing, turbulent flow exhibits highly irregular, three-dimensional patterns. This behavior arises due to instabilities in the fluid's velocity profile, and amplifies as the flow velocity increases. Minor disturbances, known as turbulent spots,...
Parallel Processing01:20

Parallel Processing

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Related Experiment Video

Updated: Jun 5, 2026

Microfluidic Imaging Flow Cytometry by Asymmetric-detection Time-stretch Optical Microscopy (ATOM)
07:19

Microfluidic Imaging Flow Cytometry by Asymmetric-detection Time-stretch Optical Microscopy (ATOM)

Published on: June 28, 2017

Next-generation acceleration and code optimization for light transport in turbid media using GPUs.

Erik Alerstam, William Chun Yip Lo, Tianyi David Han

    Biomedical Optics Express
    |January 25, 2011
    PubMed
    Summary

    This study optimized Monte Carlo (MC) simulations for light transport in biological tissues using graphics processing units (GPUs). The new GPU-MCML code achieves a 600x speedup, making complex simulations faster and more accessible.

    Keywords:
    (170.3660) Light propagation in tissues(170.5280) Photon migration(170.7050) Turbid media(290.4210) Multiple scattering

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    Generation of Heterogeneous Drug Gradients Across Cancer Populations on a Microfluidic Evolution Accelerator for Real-Time Observation

    Published on: September 19, 2019

    Area of Science:

    • Biomedical Optics
    • Computational Physics
    • Medical Imaging

    Background:

    • Monte Carlo (MC) simulations are crucial for accurate light transport modeling in biological tissues.
    • Widespread use of MC for inverse problems like photodynamic therapy (PDT) planning is hindered by long computation times.
    • Optimizing MC code for Graphics Processing Units (GPUs) presents challenges due to memory access bottlenecks.

    Purpose of the Study:

    • To develop a highly optimized Monte Carlo code package for simulating light transport on GPUs.
    • To overcome performance limitations in GPU-accelerated MC simulations.
    • To enhance the feasibility of MC simulations for complex biomedical applications.

    Main Methods:

    • Developed an optimized Monte Carlo (MC) code package, GPU-MCML, for NVIDIA Fermi GPUs.
    • Implemented an optimization scheme utilizing fast shared memory to mitigate global memory access bottlenecks.
    • Applied various optimization techniques to harness full GPU potential for light transport simulations.
    • Tested the code on a 7-layer skin model and a four-GPU cluster.

    Main Results:

    • Achieved a ~600x acceleration of the MCML code on a Fermi GPU compared to a high-end CPU.
    • Demonstrated linear performance improvement with an increasing number of GPUs in a cluster setup.
    • The GPU-MCML package is open-source and available in optimized and simplified versions.

    Conclusions:

    • The developed GPU-MCML code significantly accelerates light transport simulations.
    • GPU acceleration and optimization strategies make complex MC simulations more practical for biomedical research and applications.
    • The open-source release promotes wider adoption and further development of GPU-accelerated MC methods.