Monthly archives: April, 2017

New Paper: Role of twin and anti-phase defects in MnAl permanent magnets

Our latest paper has just been published in the journal Acta Materialia. In it, we compare finite element micromagnetics simulations to experimental evidence in order to investigate the role of twin and anti-phase defects in the reduction of performance in MnAl permanent magnets.

Room temperature (BH) max as a function of approximate raw material costs for
the theoretical MnAl permanent magnet and experimental values for a selection of common
commercial permanent magnets. The raw material costs are a good indication of the relative cost of the manufactured magnets.

The preprint version is available on


Twin domain boundary nucleation field H nuc and de-pinning fields for left H depin,L
and right H depin,R initial positions as a function of twinning angle θ.


Parallelization in Python example with joblib

It can be ridiculously easy to parallelize code in Python. Check out the following simple example:

import time 
from joblib import Parallel, delayed 

# A function that can be called to do work:
def work(arg):    
    print "Function receives the arguments as a list:", arg
    # Split the list to individual variables:
    i, j = arg    
    # All this work function does is wait 1 second...
    # ... and prints a string containing the inputs:
    print "%s_%s" % (i, j)
    return "%s_%s" % (i, j)

# List of arguments to pass to work():
arg_instances = [(1, 1), (1, 2), (1, 3), (1, 4)]
# Anything returned by work() can be stored:
results = Parallel(n_jobs=4, verbose=1, backend="threading")(map(delayed(work), arg_instances))
print results


Function receives the arguments as a list: (1, 1)
Function receives the arguments as a list: (1, 2)
Function receives the arguments as a list: (1, 3)
Function receives the arguments as a list: (1, 4)
['1_1', '1_2', '1_3', '1_4']
[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    3.9s finished

As you can see, this simple program executed all four of our argument instances sequentially, because we chose n_jobs = 1, i.e. we told it to use 1 CPU, which means it runs in series. The total time to run is reported as approximately 4 sec (it is actually less than 4 sec, but we won’t concern ourselves with this here!).

Now, we run it again in parallel, but this time with n_jobs = 4:

Function receives the arguments as a list:Function receives the arguments as a list: Function receives the arguments as a list:  Function receives the arguments as a list: (1, 1)(1, 2) (1, 4)

(1, 3)


['1_1', '1_2', '1_3', '1_4']
[Parallel(n_jobs=4)]: Done   4 out of   4 | elapsed:    0.9s finished

As you can see, the internal print commands from all four jobs are being printed to screen more-or-less simultaneously, and not in the original order. Whatever thread finished first gets printed first! The time to finish is now around 1/4 the original time, as expected.