Vectorize jacobian products across functions

[A-CGray]

Purpose

Bret Naylor from the OpenMDAO team implemented this performance improvement while helping us debug some MPhys performance issues. Essentially, it changes the calculation of the total jacobian from a series of mat-vec products in a python for loop to a single mat-mat product, which should be faster when there are a lot of outputs (e.g many thickness constraints).

Expected time until merged

No rush, we should take our time to make sure this won't break anything

Type of change

• Bugfix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (non-backwards-compatible fix or feature)
• Code style update (formatting, renaming)
• Refactoring (no functional changes, no API changes)
• Documentation update
• Maintenance update
• Other (please describe)

Testing

Checklist

• I have run flake8 and black to make sure the Python code adheres to PEP-8 and is consistently formatted
• I have formatted the Fortran code with fprettify or C/C++ code with clang-format as applicable
• I have run unit and regression tests which pass locally with my changes
• I have added new tests that prove my fix is effective or that my feature works
• I have added necessary documentation

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 67.11%. Comparing base (ce1c26f) to head (84da2e4).
⚠️ Report is 2 commits behind head on main.

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #268      +/-   ##
==========================================
- Coverage   67.11%   67.11%   -0.01%     
==========================================
  Files          48       48              
  Lines       12384    12383       -1     
==========================================
- Hits         8312     8311       -1     
  Misses       4072     4072              

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