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the compiled mexcuda function  (MathWorks Inc)


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    Structured Review

    MathWorks Inc the compiled mexcuda function
    Overall flowchart of a <t>MEXCUDA</t> function
    The Compiled Mexcuda Function, 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/result/the compiled mexcuda function/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    the compiled mexcuda function - by Bioz Stars, 2026-04
    90/100 stars

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    1) Product Images from "Optimizing photoacoustic image reconstruction using cross-platform parallel computation"

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

    Journal: Visual Computing for Industry, Biomedicine and Art

    doi: 10.1186/s42492-018-0002-5

    Overall flowchart of a MEXCUDA function
    Figure Legend Snippet: Overall flowchart of a MEXCUDA function

    Techniques Used:

    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)
    Figure Legend 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)

    Techniques Used: Blocking Assay



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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