Question

我希望并行地对一个数组进行洗牌。我发现做一个类似于bitonic排序但随机（50/50）重新排序的算法会产生相等的分布，但只有当数组是2的幂时我才算是Yates Fisher Shuffle但是我无法看到如何并行化它以避免O（N）计算。

有什么建议吗？

谢谢！

Answer 1

关于这个here的最新论文很清楚，参考资料，特别是Shun et al 2015值得一读。

但基本上你可以使用sort -R中使用的相同方法来实现这一点：通过为每一行提供一个随机键值并对该键进行排序来进行随机播放。并且有很多方法可以进行良好的并行分布式排序。

这是python + MPI中使用奇偶类型的基本版本;如果P是处理器的数量，它将通过P通信步骤。你可以做得更好，但这很容易理解;它在this question中讨论过。

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())

跑步给出：

$ 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]

并行计算 - 随机播放

1 个答案: