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gpu computing  (MathWorks Inc)


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    MathWorks Inc gpu computing
    Gpu Computing, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/gpu+computing/pm40555683-43-2-30
    Average 90 stars, based on 1 article reviews
    gpu computing - by Bioz Stars, 2026-11
    90/100 stars

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    Article Title: MATLAB package for discrete dipole approximation by graphics processing unit: Fast Fourier Transform and Biconjugate Gradient
    Article Snippet: A MATLAB package describing discrete dipole approximation (MPDDA) is introduced to calculate the optical properties of arbitrary shaped plasmonic nanoparticles (NPs).. The polarizability function, induced dipoles, and dipole interaction matrix are discussed.. To calculate the dipole moments, Fast Fourier Transform (FFT) and Biconjugate Gradient (BCG) were used to reduce the computational time and memory.

    Article Title: Programmable scanning diffuse speckle contrast imaging of cerebral blood flow
    Article Snippet: Presently, the computation time for processing 100 scanning lines ( ) is ∼ 17 s , which can be further reduced by GPU computing and leveraging MATLAB’s Parallel Computation Toolbox.

    Article Title: HyperXtract: Strategic Platform for Optimizing High-Bandwidth Nanopore Data Extraction Performance.
    Article Snippet: High-bandwidth amplifiers that offer measurement capabilities on the submicrosecond scale to resonate with shortlived events have recently gained tremendous traction in solid-state nanopore (SSN) sensing.. High acquisition rate data collection is a hallmark of such measurements, leading to oversized files.. Such experimental needs warrant high-performance analysis platforms that can analyze large files in a reasonable time frame.

    Article Title: [2408.12715] Programmable scanning diffuse speckle contrast imaging of cerebral blood flow
    Article Snippet: Presently, the computation time for processing 100 scanning lines (Table 1) is ~17 seconds, which can be further reduced by GPU computing and leveraging MATLAB’s Parallel Computation Toolbox [51].



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    CPU Intel-Core i9 and  GPU  computation time in minutes for the different cases of N using the BHM approach for solving the CH equation in  3D  with random initial condition.

    Journal: AIMS mathematics

    Article Title: Efficient numerical approaches with accelerated graphics processing unit (GPU) computations for Poisson problems and Cahn-Hilliard equations

    doi: 10.3934/math.20241334

    Figure Lengend Snippet: CPU Intel-Core i9 and GPU computation time in minutes for the different cases of N using the BHM approach for solving the CH equation in 3D with random initial condition.

    Article Snippet: Finally, we also show how to speed up 3D simulations by performing graphics processing unit (GPU) computations by using simple MATLAB commands.

    Techniques: