Parallel Computing - Shuffle - parallel-processing

Question

I am looking to shuffle an array in parallel. I have found that doing an algorithm similar to bitonic sort but with a random (50/50) re-order results in an equal distribution but only if the array is a power of 2. I've considered the Yates Fisher Shuffle but I can't see how I could parallel-ize it in order to avoid O(N) computations.
Any advice?
Thanks!

Answer 1

There's a good clear recent paper on this here and the references, especially Shun et al 2015 are worth a read.
But basically you can do this using the same sort of approach that's used in sort -R: shuffle by giving each row a random key value and sorting on that key. And there are lots of ways to do good parallel distributed sort.
Here's a basic version in python + MPI using an odd-even sort; it goes through P communication steps if P is the number of processors. You can do better than that, but this is pretty simple to understand; it's discussed in this question.
from __future__ import print_function
import sys
import random
from mpi4py import MPI
comm = MPI.COMM_WORLD
def exchange(localdata, sendrank, recvrank):
"""
Perform a merge-exchange with a neighbour;
sendrank sends local data to recvrank,
which merge-sorts it, and then sends lower
data back to the lower-ranked process and
keeps upper data
"""
rank = comm.Get_rank()
assert rank == sendrank or rank == recvrank
assert sendrank < recvrank
if rank == sendrank:
comm.send(localdata, dest=recvrank)
newdata = comm.recv(source=recvrank)
else:
bothdata = list(localdata)
otherdata = comm.recv(source=sendrank)
bothdata = bothdata + otherdata
bothdata.sort()
comm.send(bothdata[:len(otherdata)], dest=sendrank)
newdata = bothdata[len(otherdata):]
return newdata
def print_by_rank(data, rank, nprocs):
""" crudely attempt to print data coherently """
for proc in range(nprocs):
if proc == rank:
print(str(rank)+": "+str(data))
comm.barrier()
return
def odd_even_sort(data):
rank = comm.Get_rank()
nprocs = comm.Get_size()
data.sort()
for step in range(1, nprocs+1):
if ((rank + step) % 2) == 0:
if rank < nprocs - 1:
data = exchange(data, rank, rank+1)
elif rank > 0:
data = exchange(data, rank-1, rank)
return data
def main():
# everyone get their data
rank = comm.Get_rank()
nprocs = comm.Get_size()
n_per_proc = 5
data = list(range(n_per_proc*rank, n_per_proc*(rank+1)))
if rank == 0:
print("Original:")
print_by_rank(data, rank, nprocs)
# tag your data with random values
data = [(random.random(), item) for item in data]
# now sort it by these random tags
data = odd_even_sort(data)
if rank == 0:
print("Shuffled:")
print_by_rank([x for _, x in data], rank, nprocs)
return 0
if __name__ == "__main__":
sys.exit(main())
Running gives:
$ mpirun -np 5 python mergesort_shuffle.py
Original:
0: [0, 1, 2, 3, 4]
1: [5, 6, 7, 8, 9]
2: [10, 11, 12, 13, 14]
3: [15, 16, 17, 18, 19]
4: [20, 21, 22, 23, 24]
Shuffled:
0: [19, 17, 4, 20, 9]
1: [23, 12, 3, 2, 8]
2: [14, 6, 13, 15, 1]
3: [11, 0, 22, 16, 18]
4: [5, 10, 21, 7, 24]