Changes between Version 19 and Version 20 of cypress/Matlab


Ignore:
Timestamp:
08/27/2026 04:32:29 PM (8 days ago)
Author:
fuji
Comment:

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  • cypress/Matlab

    v19 v20  
    44plotting of functions and data, implementation of algorithms, and creation of user interfaces.
    55
    6 You can run your MATLAB codes on Cypress clusters, but you can't use the GUI(Graphical User Interface) on computing nodes.
     6You can run your MATLAB code on Cypress clusters, but you can't use the GUI(Graphical User Interface) on computing nodes.
    77
    88
    … …  
    1818matlab/r2022b matlab/r2023a
    1919}}}
    20 == Running MATLAB interactively in Jupyter Notebook ==
    21 See [wiki:RunningMATLABonJupyterNoteBook here].
     20
    2221
    2322== Running MATLAB interactively ==
    … …  
    6059>>
    6160}}}
    62 You will get to the MATLAB command-line and can run MATLAB code here, but '''no graphics'''.
     61You will get to the MATLAB command line and can run MATLAB code here, but '''no graphics'''.
    6362
    6463=== Running MATLAB interactively with GUI ===
    … …  
    296295}}}
    297296
     297== Running MATLAB interactively in Jupyter Notebook ==
     298See [wiki:RunningMATLABonJupyterNoteBook here].
     299
    298300=== Running MATLAB with Automatic Offload ===
     301This feature is no longer available since MATLAB R2020a. See [wiki:RunningMATLABwithXeonPhi here] to run MATLAB with Intel Xeon Phi.
     302
    299303Internally, MATLAB uses Intel MKL Basic Linear Algebra Subroutines (BLAS) and Linear Algebra package (LAPACK) routines to perform the underlying computations when running on Intel processors.
    300304
    301 Intel MKL includes Automatic Offload (AO) feature that enables computationally intensive Intel MKL functions to offload partial workload to attached '''Intel Xeon Phi''' coprocessors automatically and transparently.
     305Intel MKL includes the Automatic Offload (AO) feature that enables computationally intensive Intel MKL functions to offload partial workload to attached '''Intel Xeon Phi''' coprocessors automatically and transparently.
    302306
    303307As a result, MATLAB performance can benefit from Intel Xeon Phi coprocessors via the Intel MKL AO feature when problem sizes are large enough to amortize the cost of transferring data to the coprocessors.