# mpyll

## Installation

```pip install mpyll
```

## Usage

mpyll logic is as follows:

1. Identify the data on which to parallelize computation. The data should be stored in a list.
2. Define the task: a python function that takes as input a list of data elements and performs the desired task. This is the parallelized task; instances of this function are to be running in CPU threads.
3. Define an eventual post processing function that takes as input a list of data and returns the final result, if any.

### Example

Let's take as an example the estimation of Pi through Monte Carlo:

```import numpy as np
from mpyll import parallelize

# First, we define the data on which we would like to parallelize computation.
r = 1.
m = 10 ** 6
X = np.random.uniform(-r, r, size = m)
Y = np.random.uniform(-r, r, size = m)
data = [(X[i], Y[i]) for i in range(m)]

# Second, we define the task to be parallelized.
# It takes as input the data (a list) as well as other arguments, if any,
# and it returns a result. If it is a procedure, then it does not return.
def count_in_circle_points(data, r, m):
a = np.array(data) # matrix, each row contains a point coordinates
d = np.sqrt(np.sum(a ** 2, axis = 1)) # distance to the origin
in_circle = d <= r # an array, True if distance <= radius, False otherwise
return np.sum(in_circle)

# Finally, we define a post processor.
def estimate_pi(data, m):
pi_estimation = 4 * np.sum(data) / m
return pi_estimation

data = data, data_shuffle = False,
post_processor = estimate_pi,
n_jobs = -1,
count_in_circle_points_r = r,
count_in_circle_points_m = m,
# post processor arguments
estimate_pi_m = m)
```

### API

``````parallelize(task,
data,
shuffle_data = False,
post_processor = None,
n_jobs = -1,
*args,
**kwargs)

Parallelize a task that returns a value

Parameters
----------
data: list
The data on which the parallelization is performed.
shuffle_data: boolean
shuffle data before processing. Sometimes the data are not identically
others.
post_processor: function
A function that runs after all threads terminate.
n_jobs: int
The number of threads to be used. Specify -1 to use all CPU threads.

Other Parameters
----------------
Other parameters could be passed to `task` and `post_processor`. The argument
by an underscore and the name of the argument.

Returns
-------
If a post processor is specified, then this function returns what is returned
by the post processor, otherwise, it returns a list of the objects returned by
``````

## Project details

Uploaded `py3`