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Python Algorithms for Randomized Linear Algebra (PARLA)

PARLA is a Python package for prototyping the mathematical structure of a future C++ library for randomized numerical linear algebra. The future library is meant to be "LAPACK-like" and will organize its functionality into high-level "drivers" and lower-level "computational routines". PARLA is to be viewed as an informal, "proof of mathematical concept"-type library, serving only as a rough sketch for the future library. At the moment, PARLA is not maintained for the purposes of external use (unless per explicit requests).

The main difference between PARLA and LAPACK (aside from LAPACK being written in Fortran!) is that PARLA defines algorithms in an object-oriented way. This approach makes it easy to modify a driver's implementation by overriding the default implementation of some constituent computational routine. All driver-level functionality has passed basic tests.

PARLA has a companion Matlab library called MARLA. MARLA has a purely procedural design and isn't meant to be as general as PARLA. The state of PARLA and MARLA's APIs and unit tests is summarized in this Google Sheets spreadsheet. (It will be hard to read that spreadsheet without having the RandLAPACK design document on-hand.)

There are no plans to distribute this Python package through PyPI or conda.

How to install

The following detailed instructions assume you have Git available from the command line. You might need to modify the commands if you're running Windows or macOS.

  1. Make sure you have conda installed.
  2. Move to a directory where you can make a folder for this repo.
    • If you just want to try out notebooks, that directory might be something like ~/Desktop/temp/.
  3. Get the source code by running git clone https://github.com/rileyjmurray/parla.git.
    • That will create a folder like ~/Desktop/temp/parla that contains the contents of this repo.
    • Change directory so you're in the new folder. You should have setup.py in your working directory.
  4. Create and activate a new python environment.
    • Decide on an environment name.
      • For concreteness, I'll use the name rla39a moving forward.
    • If you have an Intel machine and want to link into MKL, do the following.
      • Run conda create --name rla39a python=3.9 pytest mkl -y,
      • Run conda activate rla39a,
      • Run conda install -c intel numpy -y,
      • Run conda install scipy jupyter matplotlib -y.
    • If you don't have an Intel machine or if you want to use OpenBLAS, do the following.
      • Run conda create --name rla39a python=3.9 pytest numpy scipy jupyter matplotlib -y.
      • Run conda activate rla39a.
  5. Install parla by running pip install -e ..
    • You need to be in the same directory that contains parla's setup.py file.
    • This command makes it possible to import parla from python, no matter your working directory.
    • The -e flag means that any edits to parla source code will be incorporated on future imports.
  6. Optional: run unittests with the command pytest parla
    • You need to be in the same directory that contains parla's setup.py file.
  7. Optional: verify that NumPy and SciPy are linked against the expected BLAS and LAPACK implementations
    • Run python -c "import numpy as np; np.show_config()"
    • The command above will probably mention MKL or OpenBLAS. If you wanted MKL and it makes any mention of OpeBLAS, then something went wrong in the installation process. Email me ([email protected]) for help.

Notes on MKL vs OpenBLAS

Unless you go out of your way to install a version of NumPy that's linked to MKL, you'll almost certainly end up with NumPy and SciPy getting linked against OpenBLAS. OpenBLAS comes with an LAPACK implementation, however, it's almost a direct copy from the Netlib LAPACK implementation. That implementation is very inefficient (all things considered) and results in unnecessarily poor performance for some Python functions that call into LAPACK (particularly, SciPy's least squares solver). I strongly recommend that you use MKL if possible.

How to uninstall

The installation process above might take up a nontrivial amount of space on your computer. For example, the Intel MKL library is around 200 megabytes. You might want to delete the python environement if you're certain that you're done working with parla. If you named your environment rla39a like above, then you'd run conda env remove --name rla39a. Make sure you are in a different python environment before running that command.

How to run Jupyter Notebooks

The following instructions are very generic. However, they assume you've gone through the installation process described above.

  1. Make sure to activate the python environment with your parla installation.
    • From the installation example, the command would be conda activate rla39a.
  2. Move to any directory on your computer that has your desired notebook somewhere in its subdirectories.
    • You don't need to have the notebook in your working directory.
  3. Run jupyter-notebook.
    • This should print out some messages and might launch a browser window.
    • The messages printed by this command should include two or three URLs; go to the last URL.

Your browser should now be running a Jupyter Notebook server. You can navigate to the notebook you want to run (such as one of these two) and launch it.