diver_182 - 2 months ago 29x

C++ Question

What parallel algorithms could I use to generate random permutations from a given set?

Especially proposals or links to papers suitable for CUDA would be helpful.

A sequential version of this would be the Fisher-Yates shuffle.

Example:

Let S={1, 2, ..., 7} be the set of source indices.

The goal is to generate n random permutations in parallel.

Each of the n permutations contains each of the source indices exactly once,

e.g. {7, 6, ..., 1}.

Answer

Fisher-Yates shuffle could be parallelized. For example, 4 concurrent workers need only 3 iterations to shuffle vector of 8 elements. On first iteration they swap 0<->1, 2<->3, 4<->5, 6<->7; on second iteration 0<->2, 1<->3, 4<->5, 6<->7; and on last iteration 0<->4, 1<->5, 2<->6, 3<->7.

This could be easily implemented as CUDA `__device__`

code (inspired by standard min/max reduction):

```
const int id = threadIdx.x;
__shared__ int perm_shared[2 * BLOCK_SIZE];
perm_shared[2 * id] = 2 * id;
perm_shared[2 * id + 1] = 2 * id + 1;
__syncthreads();
unsigned int shift = 1;
unsigned int pos = id * 2;
while(shift <= BLOCK_SIZE)
{
if (curand(&curand_state) & 1) swap(perm_shared, pos, pos + shift);
shift = shift << 1;
pos = (pos & ~shift) | ((pos & shift) >> 1);
__syncthreads();
}
```

Here the curand initialization code is omitted, and method `swap(int *p, int i, int j)`

exchanges values `p[i]`

and `p[j]`

.

Note that the code above has the following assumptions:

- The length of permutation is 2 * BLOCK_SIZE, where BLOCK_SIZE is a power of 2.
- 2 * BLOCK_SIZE integers fit into
`__shared__`

memory of CUDA device - BLOCK_SIZE is a valid size of CUDA block (usually something between 32 and 512)

To generate more than one permutation I would suggest to utilize different CUDA blocks. If the goal is to make permutation of 7 elements (as it was mentioned in the original question) then I believe it will be faster to do it in single thread.

Source (Stackoverflow)

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