matlab mex api Search Results


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MathWorks Inc mexcuda
Overall flowchart of a <t>MEXCUDA</t> function
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MathWorks Inc lasso_mex function
Overview over hardware and software requirements of the different functions for estimating L1-regularized linear models. The <t> lasso function </t> is part of the Statistics and Machine Learning Toolbox and only included here for benchmarking purposes (see main text). The lasso_mex, lasso_mexcuda and lasso_gpu functions introduced here are specifically optimized for the mass-univariate analysis approach and depend on different hardware and software configurations
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Overview over hardware and software requirements of the different functions for estimating L1-regularized linear models. The <t> lasso function </t> is part of the Statistics and Machine Learning Toolbox and only included here for benchmarking purposes (see main text). The lasso_mex, lasso_mexcuda and lasso_gpu functions introduced here are specifically optimized for the mass-univariate analysis approach and depend on different hardware and software configurations
Collision Check 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
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Overview over hardware and software requirements of the different functions for estimating L1-regularized linear models. The <t> lasso function </t> is part of the Statistics and Machine Learning Toolbox and only included here for benchmarking purposes (see main text). The lasso_mex, lasso_mexcuda and lasso_gpu functions introduced here are specifically optimized for the mass-univariate analysis approach and depend on different hardware and software configurations
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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: In details, MEXCUDA is the expansion of MATLAB mex function that utilizes C/C++ for execution using C++ MEX API.

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: In details, MEXCUDA is the expansion of MATLAB mex function that utilizes C/C++ for execution using C++ MEX API.

Techniques: Blocking Assay

Overview over hardware and software requirements of the different functions for estimating L1-regularized linear models. The  lasso function  is part of the Statistics and Machine Learning Toolbox and only included here for benchmarking purposes (see main text). The lasso_mex, lasso_mexcuda and lasso_gpu functions introduced here are specifically optimized for the mass-univariate analysis approach and depend on different hardware and software configurations

Journal: Neuroinformatics

Article Title: Fast Estimation of L1-Regularized Linear Models in the Mass-Univariate Setting

doi: 10.1007/s12021-020-09489-1

Figure Lengend Snippet: Overview over hardware and software requirements of the different functions for estimating L1-regularized linear models. The lasso function is part of the Statistics and Machine Learning Toolbox and only included here for benchmarking purposes (see main text). The lasso_mex, lasso_mexcuda and lasso_gpu functions introduced here are specifically optimized for the mass-univariate analysis approach and depend on different hardware and software configurations

Article Snippet: While the underlying coordinate descent algorithm is implemented in C++, the lasso_mex function can be conveniently called from Matlab via the mex API.

Techniques: Software