CODEWITHSUNDEEP
pythonpandaspython-multithreadingpython-multiprocessing
jax
jax

Reputation: 4207

Parallelising Python code

I have written a function that returns a Pandas data frame (sample as a row and descriptor as columns) and takes input as a list of peptides (a biological sequence as strings data). "my_function(pep_list)" takes pep_list as a parameter and return data frame. it iterates eache peptide sequence from pep_list and calculates descriptor and combined all the data as pandas data frame and returns df:

pep_list = [DAAAAEF,DAAAREF,DAAANEF,DAAADEF,DAAACEF,DAAAEEF,DAAAQEF,DAAAGEF,DAAAHEF,DAAAIEF,DAAALEF,DAAAKEF]

example:

I want to parallelising this code with the given algorithm bellow: 

1. get the number of processor available as .
    n = multiprocessing.cpu_count()

2. split the pep_list  as 
     sub_list_of_pep_list = pep_list/n 

     sub_list_of_pep_list = [[DAAAAEF,DAAAREF,DAAANEF],[DAAADEF,DAAACEF,DAAAEEF],[DAAAQEF,DAAAGEF,DAAAHEF],[DAAAIEF,DAAALEF,DAAAKEF]]

4. run "my_function()" for each core as (example if 4 cores )

     df0 = my_function(sub_list_of_pep_list[0])
     df1 = my_function(sub_list_of_pep_list[1])
     df2 = my_functonn(sub_list_of_pep_list[2])
     df3 = my_functonn(sub_list_of_pep_list[4])

5. join all df = concat[df0,df1,df2,df3] 

6. returns df with nX speed.

Please suggest me the best suitable library to implement this method.

thanks and regards.

Updated

With some reading i am able to write down a code which work as per my expectation like 1. without parallelising it takes ~10 second for 10 peptide sequence 2. with two processes it takes ~6 second for 12 peptide 3. with four processes it takes ~4 second for 12 peptides 

from multiprocessing import Process

def func1():
    structure_gen(pep_seq = ["DAAAAEF","DAAAREF","DAAANEF"])

def func2():
    structure_gen(pep_seq = ["DAAAQEF","DAAAGEF","DAAAHEF"])


def func3():
    structure_gen(pep_seq = ["DAAADEF","DAAALEF"])

def func4():
    structure_gen(pep_seq = ["DAAAIEF","DAAALEF"])

if __name__ == '__main__':
  p1 = Process(target=func1)
  p1.start()
  p2 = Process(target=func2)
  p2.start()
  p3 = Process(target=func1)
  p3.start()
  p4 = Process(target=func2)
  p4.start()
  p1.join()
  p2.join()
  p3.join()
  p4.join()

but this code easily work with 10 peptide but not able to implement it for a PEP_list contains 1 million peptide

thanks

Upvotes: 0

Views: 129

Answers (1)

swenzel
swenzel

Reputation: 7173

multiprocessing.Pool.map is what you're looking for.
Try this:

from multiprocessing import Pool

# I recommend using more partitions than processes,
# this way the work can be balanced.
# Of course this only makes sense if pep_list is bigger than
# the one you provide. If not, change this to 8 or so.
n = 50

# create indices for the partitions
ix = np.linspace(0, len(pep_list), n+1, endpoint=True, dtype=int)

# create partitions using the indices
sub_lists = [pep_list[i1:i2] for i1, i2 in zip(ix[:-1], ix[1:])]

p = Pool()
try:
    # p.map will return a list of dataframes which are to be
    # concatenated
    df = concat(p.map(my_function, sub_lists))
finally:
    p.close()

The pool will automatically contain as many processes as there are available cores. But you can overwrite this number if you want to, just have a look at the docs.

Upvotes: 3

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