pylops-mpi 0.6.1

Python library implementing linear operators with MPI

Distributed linear operators and solvers

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 Message Passing Interface (MPI) and optionally Nvidia's Collective Communication Library (NCCL) 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 via pip:

   pip install pylops-mpi
   

4. (Optional) To enable the NCCL backend for multi-GPU systems, install cupy and nccl via pip:

   pip install cupy-cudaXx nvidia-nccl-cuX
   

   with X=11,12.

Alternatively, if the Conda package manager is used to setup the Python environment, steps 1 and 2 can be skipped and mpi4py can be installed directly alongside the MPI distribution of choice:

conda install -c conda-forge mpi4py X

with X=mpich, openmpi, impi_rt, msmpi. Similarly step 4 can be accomplished using:

conda install -c conda-forge cupy nccl 

See the docs (Installation) for more information.

Run Pylops-MPI

Once you have installed the prerequisites and pylops-mpi, you can run pylops-mpi using the mpiexec command.

Here is an example on how to run a python script called <script_name>.py:

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

Example: A distributed finite-difference operator

The following example is a modified version of PyLops' README_ starting example that can handle a 2D-array distributed across ranks over the first dimension via the DistributedArray object:

import numpy as np
from pylops_mpi import DistributedArray, Partition

# Initialize DistributedArray with partition set to Scatter
nx, ny = 11, 21
x = np.zeros((nx, ny), dtype=np.float64)
x[nx // 2, ny // 2] = 1.0

x_dist = pylops_mpi.DistributedArray.to_dist(
            x=x.flatten(), 
            partition=Partition.SCATTER)

# Distributed first-derivative
D_op = pylops_mpi.MPIFirstDerivative((nx, ny), dtype=np.float64)

# y = Dx
y_dist = D_op @ x_dist

# xadj = D^H y
xadj_dist = D_op.H @ y_dist

# xinv = D^-1 y
x0_dist = pylops_mpi.DistributedArray(D_op.shape[1], dtype=np.float64)
x0_dist[:] = 0
xinv_dist = pylops_mpi.cgls(D_op, y_dist, x0=x0_dist, niter=10)[0]

Note that the DistributedArray class provides the 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 is distributed and processed across multiple nodes or processes.

Moreover, the DistributedArray class provides also fundamental mathematical operations, such as element-wise addition, subtraction, multiplication, dot product, and an equivalent of the np.linalg.norm function that operate in a distributed fashion, thus utilizing the efficiency of the MPI/NCC; protocols. This enables efficient computation and processing of large-scale distributed arrays.

Running Tests

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

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

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

Similarly, to run the NCCL test scripts in the tests_nccl folder, use the following command to run the tests:

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

Documentation

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

Contributors

• Rohan Babbar, rohanbabbar04
• Yuxi Hong, hongyx11
• Matteo Ravasi, mrava87
• Tharit Tangkijwanichakul, tharittk