gpu in matlab Search Results


matlab r2023a software  (MathWorks Inc)
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MathWorks Inc matlab r2023a software
Matlab R2023a Software, 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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plotting matlab 2018b  (MathWorks Inc)
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MathWorks Inc plotting matlab 2018b
Plotting Matlab 2018b, 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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matlab/cuda-without-c++ code (mcc)  (MathWorks Inc)
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MathWorks Inc matlab/cuda-without-c++ code (mcc)
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 <t>MATLAB,</t> performing condition checks, and allocating memory for input and output data. Device code in <t>GPU</t> is responsible for computing the radius matrix in parallel from required inputs (parallel computation block)
Matlab/Cuda Without C++ Code (Mcc), 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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gtx 1080 gpu  (MathWorks Inc)
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MathWorks Inc gtx 1080 gpu
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 <t>MATLAB,</t> performing condition checks, and allocating memory for input and output data. Device code in <t>GPU</t> is responsible for computing the radius matrix in parallel from required inputs (parallel computation block)
Gtx 1080 Gpu, 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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gpu acceleration  (MathWorks Inc)
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MathWorks Inc gpu acceleration
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 <t>MATLAB,</t> performing condition checks, and allocating memory for input and output data. Device code in <t>GPU</t> is responsible for computing the radius matrix in parallel from required inputs (parallel computation block)
Gpu Acceleration, 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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2019b gpu  (MathWorks Inc)
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MathWorks Inc 2019b gpu
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 <t>MATLAB,</t> performing condition checks, and allocating memory for input and output data. Device code in <t>GPU</t> is responsible for computing the radius matrix in parallel from required inputs (parallel computation block)
2019b Gpu, 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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matlab gpu coder  (MathWorks Inc)
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MathWorks Inc matlab gpu coder
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 <t>MATLAB,</t> performing condition checks, and allocating memory for input and output data. Device code in <t>GPU</t> is responsible for computing the radius matrix in parallel from required inputs (parallel computation block)
Matlab Gpu Coder, 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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matlab gpu program  (MathWorks Inc)
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MathWorks Inc matlab gpu program
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 <t>MATLAB,</t> performing condition checks, and allocating memory for input and output data. Device code in <t>GPU</t> is responsible for computing the radius matrix in parallel from required inputs (parallel computation block)
Matlab Gpu Program, 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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supercomputer with a 64 gb gpu and 80 gb cpu memory and matlab 2021b software  (MathWorks Inc)
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MathWorks Inc supercomputer with a 64 gb gpu and 80 gb cpu memory and matlab 2021b software
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 <t>MATLAB,</t> performing condition checks, and allocating memory for input and output data. Device code in <t>GPU</t> is responsible for computing the radius matrix in parallel from required inputs (parallel computation block)
Supercomputer With A 64 Gb Gpu And 80 Gb Cpu Memory And Matlab 2021b Software, 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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gpu-compatible operations  (MathWorks Inc)
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MathWorks Inc gpu-compatible operations
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 <t>MATLAB,</t> performing condition checks, and allocating memory for input and output data. Device code in <t>GPU</t> is responsible for computing the radius matrix in parallel from required inputs (parallel computation block)
Gpu Compatible Operations, 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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built-in gpu library  (MathWorks Inc)
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MathWorks Inc built-in gpu library
Measured peak CPU and <t> GPU </t> performance in GFLOPs for a matrix multiply experiment using <t> Matlab </t> and Jacket. The <t> GPU </t> achieves a factor of ~5 improvement over the multicore CPU.
Built In Gpu Library, 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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matlab 2014b  (MathWorks Inc)
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MathWorks Inc matlab 2014b
Measured peak CPU and <t> GPU </t> performance in GFLOPs for a matrix multiply experiment using <t> Matlab </t> and Jacket. The <t> GPU </t> achieves a factor of ~5 improvement over the multicore CPU.
Matlab 2014b, 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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Image Search Results


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: Validating images are then reconstructed using the MATLAB/CUDA-without-C++ code (MCC), MATLAB-without-GPU code (MWGC), and our MCCC in this study.

Techniques: Blocking Assay

a Comparison of reconstruction time between MCC, MCCC and MWGC and b a close look into reconstruction time difference between MCC and MCCC codes with different RF

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: a Comparison of reconstruction time between MCC, MCCC and MWGC and b a close look into reconstruction time difference between MCC and MCCC codes with different RF

Article Snippet: Validating images are then reconstructed using the MATLAB/CUDA-without-C++ code (MCC), MATLAB-without-GPU code (MWGC), and our MCCC in this study.

Techniques: Comparison

Measured peak CPU and GPU performance in GFLOPs for a matrix multiply experiment using Matlab and Jacket. The GPU achieves a factor of ~5 improvement over the multicore CPU.

Journal: International Journal of Biomedical Imaging

Article Title: Real-Time Compressive Sensing MRI Reconstruction Using GPU Computing and Split Bregman Methods

doi: 10.1155/2012/864827

Figure Lengend Snippet: Measured peak CPU and GPU performance in GFLOPs for a matrix multiply experiment using Matlab and Jacket. The GPU achieves a factor of ~5 improvement over the multicore CPU.

Article Snippet: Matlab's built-in GPU library does not support the array indexing needed for the gradient operation, so we could not compare it here.

Techniques: