FREE Star-P Technical Assessment!

Provide answers to this short questionnaire to receive a free technical assessment of how Star-P can help accelerate your MATLAB® code(s)! Interactive Supercomputing application engineers have years of experience with MATLAB and parallel programming as well as domain expertise in many technical application areas.

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Which language are you most interested in parallelizing with Star-P:

MATLAB® Python

Understanding the MATLAB code:

Do you know where in your code the compute intensive operations are located?

Yes No

Are these operations inside of a "for-loop"?

Yes No

Is/Are the "for-loop(s)" time independent? (i.e. one iteration not waiting on the previous iteration)

Yes No

Are the compute intensive operations against a large dataset(s)?

Yes No

Are datasets being loaded from databases or spreadsheets?

Yes No

Does the code leverage structures, cell arrays, and/or classes?

Yes No

Are indexing operations critical in the MATLAB code?

Yes No

About Compute Intensive Region:

What size datasets are negatively impacting performance and results — specifically in the compute intensive region of the MATLAB code?

Additional comments:

<10 Megabytes >10 but <100 Megabytes >100 Megabytes to 1 Gigabyte Greater then a Gigabyte

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How long is the compute intensive region of the code taking to run or estimated to run?

Additional comments:

Seconds Minutes Hours More than 8 hours

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Compute Intensive operations involve:

Additional comments:

Vectors Matricies Elements

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Which of these is used in the most compute intensive regions of the code?

Additional comments:

Core MATLAB Statistics Toolbox Image Processing Toolbox Signal Processing Toolbox Optimization Toolbox Sparse Matricies Other Toolboxes

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Are there commercial, open source or internally developed libraries that could benefit the compute intensive regions of the code? (i.e. written in C, C++ or Fortran)

Additional comments:

Yes No