AA-sort
A co-worker sent me a link to AA-sort some time ago, but I didn't have any reason to use it until just recently. I found myself in a situation where I have 2K~4K 4-byte elements packet into qwords and my initial O(N^2) solution just wasn't working out. So, I decided to try the O(N log N) "in-core" component of AA-sort as a pre-sort.
Next come cmpswap() and cmpswap_skew() from the paper:
In both functions I return the comparison mask because I think it's necessary to implement step 2 of AA-sort's in-core algorithm; the modified combsort:
I use a shrink factor of 1.3 as suggested by the authors as well the combsort page at Wikipedia. Standard combsort stops looping once gap is less-or-equal to 1 and no swaps have been performed in the current iteration. AA-sort splits the gap and "swap" loops so I use the not_sorted value to keep track of when swaps stop occurring (this is the reason that I return the cmp value from each of the swap() functions).
You end up with nicely sorted (albeit transposed) data like:
AA-sort normally calls for a final pass that transposes the elements across the width of each vector rather than through each vector element but it wasn't necessary in my particular situation.
Pretty decent considering the original O(N^2) implementation was taking 3 ms for 2k+ values.
The paper linked above is relatively straight-forward, but they only provide pseudo-code so there's still a bit of effort required to get something usable. As per the author's goals, it's easily written as highly-vectorized, non-branching code and was rather fun to get working.
The authors were working with 8 16-bit values packed into a qword, and I had to make some simple changes to get it working with vec_uint4s.
First, my bitonic_sort() routine takes care of step one of their in-core algorithm by sorting the 4 uint32_t elements in a qword:
inline vec_uint4 bitonic_sort( vec_uint4 Vec_ )
{
static const vec_uchar16 BADC = {
0x04, 0x05, 0x06, 0x07, 0x00, 0x01, 0x02, 0x03,
0x0c, 0x0d, 0x0e, 0x0f, 0x08, 0x09, 0x0a, 0x0b
};
static const vec_uchar16 CDAB = {
0x08, 0x09, 0x0a, 0x0b, 0x0c, 0x0d, 0x0e, 0x0f,
0x00, 0x01, 0x02, 0x03, 0x04, 0x05, 0x06, 0x07
};
static const vec_uint4 mask1 = {
0x00000000, 0xffffffff, 0xffffffff, 0x00000000
};
static const vec_uint4 mask2 = {
0x00000000, 0x00000000, 0xffffffff, 0xffffffff
};
static const vec_uint4 mask3 = {
0x00000000, 0xffffffff, 0x00000000, 0xffffffff
};
vec_uint4 temp, cmp, result;
temp = spu_shuffle( Vec_, Vec_, BADC ); // Compare A to B and C to D
cmp = spu_cmpgt( Vec_, temp );
cmp = spu_xor( cmp, mask1 ); // Order A/B increasing, C/D decreasing
result = spu_sel( Vec_, temp, cmp );
temp = spu_shuffle( result, result, CDAB ); // Compare AB to CD
cmp2 = spu_cmpgt( result, temp );
cmp2 = spu_xor( cmp2, mask2 ); // Order AB/CD increasing
result = spu_sel( result, temp, cmp2 );
temp = spu_shuffle( result, result, BADC ); // Compare previous A to B and C to D
cmp = spu_cmpgt( result, temp );
cmp = spu_xor( cmp, mask3 ); // Order A/B and C/D increasing
result = spu_sel( result, temp, cmp );
return result;
}
Next come cmpswap() and cmpswap_skew() from the paper:
inline vec_uint4 vector_cmpswap( vec_uint4* A_, vec_uint4* B_ )
{
const vec_uint4 a = *A_;
const vec_uint4 b = *B_;
const vec_uint4 cmp = spu_cmpgt( a, b );
*A_ = spu_sel( a, b, cmp );
*B_ = spu_sel( b, a, cmp );
return cmp;
}
inline vec_uint4 vector_cmpswap_skew( vec_uint4* A_, vec_uint4* B_ )
{
const vec_uint4 a = *A_;
const vec_uint4 b = *B_;
// The last word compared is 0xffff so that part of the mask should always be 0000
static const vec_uchar16 BCDx = {
0x04, 0x05, 0x06, 0x07, 0x08, 0x09, 0x0a, 0x0b,
0x0c, 0x0d, 0x0e, 0x0f, 0xc0, 0xc0, 0xc0, 0xc0
};
const vec_uint4 bShift = spu_shuffle( b, b, BCDx );
const vec_uint4 cmp = spu_cmpgt( a, bShift );
const vec_uint4 swap = spu_sel( a, bShift, cmp );
const vec_uint4 bSwap = spu_sel( bShift, a, cmp );
static const vec_uchar16 abcD = {
0x10, 0x11, 0x12, 0x13, 0x14, 0x15, 0x16, 0x17,
0x18, 0x19, 0x1a, 0x1b, 0x0c, 0x0d, 0x0e, 0x0f
};
static const vec_uchar16 Aabc = {
0x00, 0x01, 0x02, 0x03, 0x10, 0x11, 0x12, 0x13,
0x14, 0x15, 0x16, 0x17, 0x18, 0x19, 0x1a, 0x1b
};
*A_ = spu_shuffle( a, swap, abcD );
*B_ = spu_shuffle( b, bSwap, Aabc );
return cmp;
}
In both functions I return the comparison mask because I think it's necessary to implement step 2 of AA-sort's in-core algorithm; the modified combsort:
static const float INV_SHRINK_FACTOR = 1.f/1.3f;
uint32_t gap = uint32_t(float(edgeCount) * INV_SHRINK_FACTOR);
while (gap > 1)
{
// Straight comparisons
const uint32_t maxSwapIndex = edgeCount - gap;
for (uint32_t i = 0; i < maxSwapIndex; ++i)
{
vector_cmpswap( (vec_uint4*)&work[i], (vec_uint4*)&work[i+gap] );
}
// Skewed comparisons for when i+gap exceeds N/4
for (uint32_t i = edgeCount - gap; i < edgeCount; ++i)
{
vector_cmpswap_skew( (vec_uint4*)&work[i], (vec_uint4*)&work[i+gap - edgeCount] );
}
gap = uint32_t((float)gap * INV_SHRINK_FACTOR);
}
vec_uint4 not_sorted;
do
{
not_sorted = spu_splats((uint32_t)0);
for (uint32_t i = 0; i < edgeCount - 1; ++i)
{
not_sorted = spu_or( not_sorted, vector_cmpswap( (vec_uint4*)&work[i], (vec_uint4*)&work[i+1] ) );
}
not_sorted = spu_or( not_sorted, vector_cmpswap_skew( (vec_uint4*)&work[edgeCount - 1], (vec_uint4*)&work[0] ) );
not_sorted = spu_orx( not_sorted );
} while ( spu_extract(not_sorted, 0) );
I use a shrink factor of 1.3 as suggested by the authors as well the combsort page at Wikipedia. Standard combsort stops looping once gap is less-or-equal to 1 and no swaps have been performed in the current iteration. AA-sort splits the gap and "swap" loops so I use the not_sorted value to keep track of when swaps stop occurring (this is the reason that I return the cmp value from each of the swap() functions).
You end up with nicely sorted (albeit transposed) data like:
[0] = 00000003 03910392 04a504a6 05140515
[1] = 0000002b 03910393 04a604a7 05140516
[2] = 0000002b 03910393 04a7049f 05150514
[3] = 0000002c 03910394 04a704a8 05150516
[4] = 0000002c 03920393 04a704a8 05150516
[5] = 0000002d 03930394 04a804a9 05160515
[6] = 0003002b 03b903ba 04a9049f 05170516
[7] = 00200021 03b903bb 04a904aa 05170518
[8] = 00200022 03b903bb 04a904aa 05180517
[9] = 00210022 03b903bc 04aa04ab 05190518
...
AA-sort normally calls for a final pass that transposes the elements across the width of each vector rather than through each vector element but it wasn't necessary in my particular situation.
I took some performance statistics with the decrementer using a few of my data sets and attached the results below:
Time (ms) | # Values |
---|---|
0.036667 | 1254 |
0.008571 | 171 |
0.017419 | 684 |
0.131654 | 1902 |
0.019900 | 786 |
0.009424 | 360 |
0.109599 | 954 |
0.347531 | 2415 |
0.371779 | 2454 |
Pretty decent considering the original O(N^2) implementation was taking 3 ms for 2k+ values.