pylops-mpi 0.1.0

Python library implementing linear operators with MPI

PyLops MPI

pylops-mpi is a Python library built on top of PyLops, designed to enable distributed and parallel processing of large-scale linear algebra operations and computations.

Installation

To install pylops-mpi, you need to have MPI (Message Passing Interface) installed on your system.

1. Download and Install MPI: Visit the official MPI website to download an appropriate MPI implementation for your system. Follow the installation instructions provided by the MPI vendor.

   • Open MPI
   • MPICH
   • Intel MPI

2. Verify MPI Installation: After installing MPI, verify its installation by opening a terminal or command prompt and running the following command:

   mpiexec --version
   

3. Install pylops-mpi: Once MPI is installed and verified, you can proceed to install pylops-mpi.

   You can install with pip:

   pip install pylops-mpi
   

   You can install with make and conda:

   make install_conda
   

Run Pylops-MPI

Once you have installed the prerequisites and pylops-mpi, you can run pylops-mpi using the mpiexec command. Here's an example on how to run the command:

mpiexec -n <NUM_PROCESSES> python <script_name>.py

Example

The DistributedArray can be used to either broadcast or scatter the NumPy array across different ranks or processes.

from pylops_mpi import DistributedArray, Partition

global_shape = (10, 5)

# Initialize a DistributedArray with partition set to Broadcast
dist_array_broadcast = DistributedArray(global_shape=global_shape,
                                        partition=Partition.BROADCAST)

# Initialize a DistributedArray with partition set to Scatter
dist_array_scatter = DistributedArray(global_shape=global_shape,
                                      partition=Partition.SCATTER)

Additionally, the DistributedArray can be used to scatter the array along any specified axis.

# Partition axis = 0
dist_array_0 = DistributedArray(global_shape=global_shape, 
                                partition=Partition.SCATTER, axis=0)

# Partition axis = 1
dist_array_1 = DistributedArray(global_shape=global_shape, 
                                partition=Partition.SCATTER, axis=1)

The DistributedArray class provides a to_dist class method that accepts a NumPy array as input and converts it into an instance of the DistributedArray class. This method is used to transform a regular NumPy array into a DistributedArray that can be distributed and processed across multiple nodes or processes.

import numpy as np
np.random.seed(42)

dist_arr = DistributedArray.to_dist(x=np.random.normal(100, 100, global_shape), 
                                    partition=Partition.SCATTER, axis=0)

The DistributedArray also provides fundamental mathematical operations, like element-wise addition, subtraction, and multiplication, as well as dot product and the np.linalg.norm function in a distributed fashion, thus utilizing the efficiency of the MPI protocol. This enables efficient computation and processing of large-scale distributed arrays.

Running Tests

The test scripts are located in the tests folder. Use the following command to run the tests:

mpiexec -n <NUM_PROCESSES> pytest --with-mpi

The --with-mpi option tells pytest to enable the pytest-mpi plugin, allowing the tests to utilize the MPI functionality.

Documentation

The official documentation of Pylops-MPI is available here. Visit the official docs to learn more about pylops-mpi.

Contributors

• Rohan Babbar, rohanbabbar04
• Matteo Ravasi, mrava87