I wrote code like this:
def process(data):
#create file using data
all = ["data1", "data2", "data3"]
I want to execute process function on my all list in parallel, because they are creating small files so I am not concerned about disk write but the processing takes long, so I want to use all of my cores.
How can I do this using default modules in python 2.7?
Assuming CPython and the GIL here.
If your task is I/O bound, in general, threading may be more efficient since the threads are simply dumping work on the operating system and idling until the I/O operation finishes. Spawning processes is a heavy way to babysit I/O.
However, most file systems aren't concurrent, so using multithreading or multiprocessing may not be any faster than synchronous writes.
Nonetheless, here's a contrived example of multiprocessing.Pool.map which may help with your CPU-bound work:
from multiprocessing import cpu_count, Pool
def process(data):
# best to do heavy CPU-bound work here...
# file write for demonstration
with open("%s.txt" % data, "w") as f:
f.write(data)
# example of returning a result to the map
return data.upper()
tasks = ["data1", "data2", "data3"]
pool = Pool(cpu_count() - 1)
print(pool.map(process, tasks))
A similar setup for threading can be found in concurrent.futures.ThreadPoolExecutor.
As an aside, all is a builtin function and isn't a great variable name choice.