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mexcuda  (MathWorks Inc)


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    MathWorks Inc mexcuda
    Mexcuda, 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/mexcuda/10__1016_slash_j__medengphy__2024__104268-204-18-22
    Average 90 stars, based on 1 article reviews
    mexcuda - by Bioz Stars, 2026-09
    90/100 stars

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    Related Articles

    Imaging:

    Article Title: Trends and Developments in 3D Photoacoustic Imaging Systems: A Review of Recent Progress
    Article Snippet: Photoacoustic imaging (PAI) is a non-invasive diagnostic imaging technique that utilizes the photoacoustic effect by combining optical and ultrasound imaging systems.. The development of PAI is mostly centered on the generation of a high-quality 3D reconstruction system for more optimal and accurate identification of tissue abnormalities.. This literature study was conducted to analyze the 3D image reconstruction in PAI over 2017–2024.

    other:

    Article Title: Optimizing photoacoustic image reconstruction using cross-platform parallel computation
    Article Snippet: In details, MEXCUDA is the expansion of MATLAB mex function that utilizes C/C++ for execution using C++ MEX API.

    Article Title: Optimizing photoacoustic image reconstruction using cross-platform parallel computation
    Article Snippet: The workflow of a function execution by MEXCUDA is demonstrated in Fig. . First, in the MATLAB front-end code, users load raw data, convert CPU-based matrices into GPU matrices, and set reconstruction parameters.



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    Image Search Results


    Overall flowchart of a MEXCUDA function

    Journal: Visual Computing for Industry, Biomedicine and Art

    Article Title: Optimizing photoacoustic image reconstruction using cross-platform parallel computation

    doi: 10.1186/s42492-018-0002-5

    Figure Lengend Snippet: Overall flowchart of a MEXCUDA function

    Article Snippet: From this source code, we create the compiled MEXCUDA function by using the mexcuda command in MATLAB.

    Techniques:

    Example process flow of the heterogeneous computing. In this figure, we create a sample MEXCUDA function for calculating the radius matrix of a scanned region to each transducer elements. The host code (CPU) is in charge of initialization and finalization blocks such as reading the input from MATLAB, performing condition checks, and allocating memory for input and output data. Device code in GPU is responsible for computing the radius matrix in parallel from required inputs (parallel computation block)

    Journal: Visual Computing for Industry, Biomedicine and Art

    Article Title: Optimizing photoacoustic image reconstruction using cross-platform parallel computation

    doi: 10.1186/s42492-018-0002-5

    Figure Lengend Snippet: Example process flow of the heterogeneous computing. In this figure, we create a sample MEXCUDA function for calculating the radius matrix of a scanned region to each transducer elements. The host code (CPU) is in charge of initialization and finalization blocks such as reading the input from MATLAB, performing condition checks, and allocating memory for input and output data. Device code in GPU is responsible for computing the radius matrix in parallel from required inputs (parallel computation block)

    Article Snippet: From this source code, we create the compiled MEXCUDA function by using the mexcuda command in MATLAB.

    Techniques: Blocking Assay

    Overall flowchart of a MEXCUDA function

    Journal: Visual Computing for Industry, Biomedicine and Art

    Article Title: Optimizing photoacoustic image reconstruction using cross-platform parallel computation

    doi: 10.1186/s42492-018-0002-5

    Figure Lengend Snippet: Overall flowchart of a MEXCUDA function

    Article Snippet: We first need to generate a MEXCUDA function before calling it in MATLAB.

    Techniques:

    Example process flow of the heterogeneous computing. In this figure, we create a sample MEXCUDA function for calculating the radius matrix of a scanned region to each transducer elements. The host code (CPU) is in charge of initialization and finalization blocks such as reading the input from MATLAB, performing condition checks, and allocating memory for input and output data. Device code in GPU is responsible for computing the radius matrix in parallel from required inputs (parallel computation block)

    Journal: Visual Computing for Industry, Biomedicine and Art

    Article Title: Optimizing photoacoustic image reconstruction using cross-platform parallel computation

    doi: 10.1186/s42492-018-0002-5

    Figure Lengend Snippet: Example process flow of the heterogeneous computing. In this figure, we create a sample MEXCUDA function for calculating the radius matrix of a scanned region to each transducer elements. The host code (CPU) is in charge of initialization and finalization blocks such as reading the input from MATLAB, performing condition checks, and allocating memory for input and output data. Device code in GPU is responsible for computing the radius matrix in parallel from required inputs (parallel computation block)

    Article Snippet: We first need to generate a MEXCUDA function before calling it in MATLAB.

    Techniques: Blocking Assay

    Overall flowchart of a MEXCUDA function

    Journal: Visual Computing for Industry, Biomedicine and Art

    Article Title: Optimizing photoacoustic image reconstruction using cross-platform parallel computation

    doi: 10.1186/s42492-018-0002-5

    Figure Lengend Snippet: Overall flowchart of a MEXCUDA function

    Article Snippet: It maintains the simplicity of MATLAB, while improves the speed through CUDA/C++ − based MATLAB converted functions called MEXCUDA.

    Techniques:

    Example process flow of the heterogeneous computing. In this figure, we create a sample MEXCUDA function for calculating the radius matrix of a scanned region to each transducer elements. The host code (CPU) is in charge of initialization and finalization blocks such as reading the input from MATLAB, performing condition checks, and allocating memory for input and output data. Device code in GPU is responsible for computing the radius matrix in parallel from required inputs (parallel computation block)

    Journal: Visual Computing for Industry, Biomedicine and Art

    Article Title: Optimizing photoacoustic image reconstruction using cross-platform parallel computation

    doi: 10.1186/s42492-018-0002-5

    Figure Lengend Snippet: Example process flow of the heterogeneous computing. In this figure, we create a sample MEXCUDA function for calculating the radius matrix of a scanned region to each transducer elements. The host code (CPU) is in charge of initialization and finalization blocks such as reading the input from MATLAB, performing condition checks, and allocating memory for input and output data. Device code in GPU is responsible for computing the radius matrix in parallel from required inputs (parallel computation block)

    Article Snippet: It maintains the simplicity of MATLAB, while improves the speed through CUDA/C++ − based MATLAB converted functions called MEXCUDA.

    Techniques: Blocking Assay