CODEWITHSUNDEEP
c++x86ssesimdavx
Wibowit
Wibowit

Reputation: 371

Find index of maximum element in x86 SIMD vector

I'm thinking of implementing 8-ary heapsort for uint32_t's. To do this I need a function that selects the index of maximum element in a 8-element vector so that I can compare it with parent element and conditionally perform swap and further siftDown steps.

(8 uint32_ts can be changed eg to 16 uint32_ts or 8 uint64_t or whatever x86 SIMD could support efficiently).

I have some ideas on how to do that but I'm looking for something faster than non-vectorized code, especially I'm looking for something that would enable me to do fast heapsort.

I have clang++ 3.3 and Core i7-4670 so probably I should be able to use even the newest x86 SIMD thingies.

(BTW: that's a part of a bigger project: https://github.com/tarsa/SortingAlgorithmsBenchmark and there's for example quaternary heapsort so after implementing SIMD heapsort I could instantly compare them)

To repeat - question is: what's the most efficient way to compute index of maximum element in x86 SIMD vector?

PS: It's not a duplicate of linked questions - notice that I'm asking for an index of a maximum element, not just the element value.

Upvotes: 14

Views: 7734

Answers (3)

Paul R
Paul R

Reputation: 212969

Horizontal operations are bad news with SIMD, and particularly so with AVX, where most 256 bit instructions are actually broken into two separate 128 bit operations. Having said that, if you really must do a horizontal 32 bit max across 8 elements then I think the general approach would have to be:

  • find the max value (typically several shift/permute and max operations)
  • splat the max value across all 8 elements of a second vector (can be combined with previous operation)
  • do a compare between the original vector and the max vector (_mm256_cmpeq_epi32)
  • extract scalar mask (_mm256_movemask_epi8)
  • convert scalar mask to index

Here is a first pass at an AVX2 implementation I just put together - I tested it and benchmarked it on a 2.6 GHz Haswell and it runs at around 1.7 ns / vector (including loading the vector and storing the resulting index):

uint8_t _mm256_hmax_index(const __m256i v)
{
    __m256i vmax = v;

    vmax = _mm256_max_epu32(vmax, _mm256_alignr_epi8(vmax, vmax, 4));
    vmax = _mm256_max_epu32(vmax, _mm256_alignr_epi8(vmax, vmax, 8));
    vmax = _mm256_max_epu32(vmax, _mm256_permute2x128_si256(vmax, vmax, 0x01));

    __m256i vcmp = _mm256_cmpeq_epi32(v, vmax);

    uint32_t mask = _mm256_movemask_epi8(vcmp);

    return __builtin_ctz(mask) >> 2;
}

Upvotes: 15

Vir
Vir

Reputation: 459

Using the Vc library I'd write:

size_t maximumIndex(Vc::uint_v vec) {
  const unsigned int max = vec.max();
  return (max == vec).firstOne();
}

With intrinsics it should be something along these lines (this is AVX without AVX2 - with AVX2 it becomes slightly easier):

size_t maximumIndex(_mm256i vec) {
  __m128i lo = _mm256_castsi256_si128(vec);
  __m128i hi = _mm256_extractf128_si256(vec, 1);
  __m128i tmp = _mm_max_epu32(lo, hi);
  tmp = _mm_max_epu32(tmp, _mm_shuffle_epi32(tmp, _MM_SHUFFLE(1, 0, 3, 2)));
  tmp = _mm_max_epu32(tmp, _mm_shufflelo_epi16(tmp, _MM_SHUFFLE(1, 0, 3, 2))); // using lo_epi16 for speed here
  const int max = _mm_cvtsi128_si32(tmp);
  tmp = _mm_packs_epi16(_mm_packs_epi32(_mm_cmpeq_epi32(_mm_set1_epi32(max), lo),
                                        _mm_cmpeq_epi32(_mm_set1_epi32(max), hi)),
                        _mm_setzero_si128());
  return _bit_scan_forward(_mm_movemask_epi8(tmp));
}

BTW, if you want some inspiration from SIMDized merge-sort look here: http://code.compeng.uni-frankfurt.de/projects/vc/repository/revisions/master/entry/src/avx_sorthelper.cpp

Upvotes: 2

Sneftel
Sneftel

Reputation: 41474

The most efficient way to do a horizontal operation (dot product, sum, max-index, whatever) on an n-way SIMD vector is to do n of them at once, by transposing them and using vertical ops instead. Certain SIMD architectures have better support for horizontal ops, but in general the blockwise transposed approach will be more flexible and efficient.

Upvotes: 6

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