# PyLops-distributed¶

Note

This library is under early development.

Expect things to constantly change until version v1.0.0.

This library is an extension of PyLops for distributed operators.

As much as numpy and scipy lie at the core of the parent project PyLops, PyLops-distributed heavily builds on top of Dask, and more specifically Dask arrays.

Doing so, linear operators can be parallelized across several processes on a single node or across multiple nodes. Their forward and adjoint are first lazily built as directed acyclic graphs and evaluated only when requested by the user (or automatically within one of our solvers).

Most of the operators and solvers in PyLops-distributed mirror their equivalents in PyLops and users can seamlessly switch between PyLops and PyLops-distributed or even combine operators acting locally with distributed operators.

Here is a simple example showing how a diagonal operator can be created, applied and inverted using PyLops:

import numpy as np
from pylops import Diagonal

n = 10
x = np.ones(n)
d = np.arange(n) + 1

Dop = Diagonal(d)

# y = Dx
y = Dop*x
# x = D'y
# xinv = D^-1 y
xinv = Dop / y


and similarly using PyLops-distributed:

import numpy as np
import pylops_distributed
from pylops_distributed import Diagonal

# set-up client

n = 10
x = da.ones(n, chunks=(n//2,))
d = da.from_array(np.arange(n) + 1, chunks=(n//2, n//2))

Dop = Diagonal(d)

# y = Dx
y = Dop*x
# x = D'y
# xinv = D^-1 y
xinv = Dop / y


• In this specific case we did not even need to reimplement the Diagonal operator. Calling numpy operations as methods (e.g., x.sum()) instead of functions (e.g., np.sum(x)) makes it automatic for our operator to act as a distributed operator when a dask array is provided instead. Unfortunately not all numpy functions are also implemented as methods: in those cases we reimplement the o perator directly within PyLops-distributed.
• Using * and .H* is still possible also within PyLops-distributed, however when initializing an operator we will need to decide whether we want to simply create dask graph or also evaluation. This gives flexibility as we can decide if and when apply evaluation using the compute method on a dask array of choice.