Bit-permuting 16 u32s at once with AVX-512

The basic trick to apply the same bit-permutation to each of the u32s is to view them as matrix of 16 rows by 32 columns, transpose it into a 32 u16s, permute those u16s in the same way that we wanted to permute the bits of the u32s [1], then transpose back to 16 u32s. Easy:

__m512i permbits_16x32(__m512i data, __m512i indices)
{
    __m512i x = data;
    x = transpose_16_dwords_to_32_words(x);
    x = _mm512_permutexvar_epi16(indices, x);
    x = transpose_32_words_to_16_dwords(x);
    return x;
}

transpose_16_dwords_to_32_words and transpose_32_words_to_16_dwords may be tricky to implement by hand, but haroldbot.nl/avx512bpc.html can solve them. In order to transpose 16 dwords into 32 words, the 4 most significant bits of every bit-index (the 9-bit bit-index of every bit in a 512-bit __m512i) should become the 4 least significant bits, and the 5 least significant bits of the index should become the most significant bits. In other words, the bit order of the bit-indices should become 5,6,7,8,0,1,2,3,4. For the inverse, 4,5,6,7,8,0,1,2,3.

Taking the solutions from that AVX512 BPC permute solver and making them the bread of the transpose-shuffle-transpose sandwich is a valid solution, but there is an opportunity for improvement: that sandwich ends up with 3 back-to-back permutes in the middle, perhaps they can be merged somehow.

One solution I've found, is to use the bit-index-bit-order 5,6,7,0,1,2,3,4,8. Keeping the most-significant bit in place simplifies the first "transpose" to no longer need a shuffle at the end, but now instead of shuffling each pair of adjacent bytes the same way (which a permutation of 16-bit elements accomplishes for free) we need to shuffle the low and high half of a 64-byte vector the same way, which requires some pre-processing of the index-vector: duplicate a 32-byte vector into the top and bottom of a 64-byte vector, and add 32 to every byte in the top half. The second not-quite-transpose in the new sandwich, corresponding to a bit-order of 3,4,5,6,7,0,1,2,8, starts with a simple permute: byte-reverse every u64. This permute can be absorbed into the pre-processing of the index-vector. In general that would not help, but in cases in which the same index-vector is reused multiple times (probably a common usage pattern, I've already used it that way myself) moving that permute into the pre-processing step can move it out of the loop.

The code

Putting it all together, permbits_16x32 can be implemented like this:

__m512i permbits_16x32_weirdindex(__m512i x, __m512i p) {
    
    __m512i mID = _mm512_set1_epi64(0x8040201008040201);
    __m512i s1 = _mm512_set_epi8(
        35, 39, 43, 47, 51, 55, 59, 63,
        34, 38, 42, 46, 50, 54, 58, 62,
        33, 37, 41, 45, 49, 53, 57, 61,
        32, 36, 40, 44, 48, 52, 56, 60,
        3, 7, 11, 15, 19, 23, 27, 31,
        2, 6, 10, 14, 18, 22, 26, 30,
        1, 5, 9, 13, 17, 21, 25, 29,
        0, 4, 8, 12, 16, 20, 24, 28);
    __m512i s2 = _mm512_set_epi8(
        63, 55, 47, 39, 62, 54, 46, 38,
        61, 53, 45, 37, 60, 52, 44, 36,
        59, 51, 43, 35, 58, 50, 42, 34,
        57, 49, 41, 33, 56, 48, 40, 32,
        31, 23, 15, 7, 30, 22, 14, 6,
        29, 21, 13, 5, 28, 20, 12, 4,
        27, 19, 11, 3, 26, 18, 10, 2,
        25, 17, 9, 1, 24, 16, 8, 0);
    x = _mm512_permutexvar_epi8(s1, x);
    x = _mm512_gf2p8affine_epi64_epi8(mID, x, 0);
    x = _mm512_permutexvar_epi8(p, x);
    x = _mm512_gf2p8affine_epi64_epi8(mID, x, 0);
    x = _mm512_permutexvar_epi8(s2, x);
    return x;
}

__m512i permbits_16x32(__m512i x, __m256i indices)
{
    __m512i s1 = _mm512_set_epi8(
        24, 25, 26, 27, 28, 29, 30, 31,
        16, 17, 18, 19, 20, 21, 22, 23,
        8, 9, 10, 11, 12, 13, 14, 15,
        0, 1, 2, 3, 4, 5, 6, 7,
        24, 25, 26, 27, 28, 29, 30, 31,
        16, 17, 18, 19, 20, 21, 22, 23,
        8, 9, 10, 11, 12, 13, 14, 15,
        0, 1, 2, 3, 4, 5, 6, 7);
    __m512i p = _mm512_permutexvar_epi8(s1, _mm512_castsi256_si512(indices));
    uint64_t m = 0x2020202020202020;
    p = _mm512_add_epi8(p, _mm512_set_epi64(m, m, m, m, 0, 0, 0, 0));
    return permbits_16x32_weirdindex(x, p);
}

---

[1]: As a bonus, you can put other permutation-like operations between the transposes, such as vpcompressd to perform a pext on every u32 (but with only one shared mask used for all 16 u32s), or even operations that are not permutation-like.

Histogramming bytes with positional popcount (GF2P8AFFINEQB edition)

A while ago, after some back and forth on twitter/X with @corsix, I dropped some implementation of byte histogramming without explaining anything. This post aims to rectify that lack of explanation.

pospopcnt

Imagine an array of N k-bit words as an N-by-k matrix of bits. It's easy to take a plain old popcnt across a row of the matrix. pospopcnt is the count per position, ie summing across a column.

The amount of "heavy vertical summation" (assuming that it is a heavy operation) can be reduced by putting the rows through some "vertical" carry-save adders (which works out to some cheap bitwise operations, especially cheap on AVX512 / AVX10 thanks to ternlog), this technique is also discussed in for example Efficient Computation of Positional Population Counts Using SIMD Instructions.

That reduction, though efficient, produces an intermediate result in an awkward format. If we have four 512-bit vectors as the intermediate result and are taking the pospopcnt of 64-bit QWORDs, each of the vectors now holds 8 groups of QWORDs, where the k'th bit of each QWORD in the i'th vector contributes a value of 2ⁱ to the k'th position of the pospopcnt. "Efficient Computation of Positional Population Counts Using SIMD Instructions" and the associated code show various ways to do this without using GF2P8AFFINEQB. That paper and code have a context of pospopcnt-ing 16-bit words, which makes GF2P8AFFINEQB-free approaches more viable.

With GF2P8AFFINEQB (and VPERMB), the bits in each vector can be regouped so that the 8 k'th bit of each QWORD are grouped together in the k'th byte of the vector. VPOPCNTB easily sums those bits, from here on all we need to do is shift each vector by i and add them up.

Now that we have a pospopcnt, we can use that to make a histogram of some data after transforming that data via 1 << data.

Binning

The pospopcnt as I described it in the previous paragraph only produces 64 counts, so it's suitable for making a histogram of data in the range from 0 up to 64. For bytes, the pospopcnt could be generalized to count across 256 positions, which is how one of my early prototypes of this algorithm worked. Doing it that way results in an somewhat awkward left shift (it's surprisingly not-horrible to set the k'th bit of a 256-bit vector, but still more awkward than just VPSLLVQ-ing 1 by the data), and in having to reduce/sum vectors in which only 1 out of every 256 bits is set. The 64-bit case already suffers from mostly summing zeroes, with the reduction operations mostly reducing nothing to nothing, and the 256-bit case makes that 4 times as bad.

For a while I thought that limited the applicability of the "pospopcnt histogram algorith" to 6-bit data, but then I had another idea: if the data is not already in the range 0..64, split it into bins that are. As long as that can be done quickly enough, and it turned out that it can be done quickly enough based on VPCOMPRESSB, the extra time spent on binning the data pays for itself by enabling the use of the 64-bit pospopcnt rather than its slow 256-bit generalization.

Extras

According to my own benchmarks on an Intel 11600K (Rocket Lake), this algorithm is almost twice as fast as Powturbo's hist_8_64 on both random and non-random data, reaching about 8.8GB/s vs 4.6GB/s for hist_8_64 either way, being mostly immune to patterns in the data (unlike the good old hist[data[i]]++ algorithm, which is infamous for slowing down for some kinds of patterned data).

Benchmarks have also been done on Zen5 (9700X), which as a side effect also make my 11600K look like a joke. Interestingly, from this benchmark it looks like some kinds of patterned data increase performance on Zen5, though I did not see that effect on my 11600K.

Below is a diagram summarizing the major components of the algorithm.

Enumerating identities, part 2

Part 2 of Enumerating all mathematical identities (in fixed-size bitvector arithmetic with a restricted set of operations) of a certain size.

To recap, the approach in part 1 was broadly:

• Use a CEGIS-style loop to find a pair of expressions that are equal for all possible inputs.
• After finding such a pair, block it from being found again.
• Repeat until no more pairs can be found.

One weakness (in addition to the other weaknesses) of that approach is that the "block list" keeps growing as more identities are found. Here is an alternative approach that does not use an ever-growing block list:

• Interpreting the raw bit-string that represents a pair of expressions as an integer, find the lowest pair of equivalent expressions.
• After finding the lowest pair, interpreted as the number X, set the lower bound for the next pair to X + 1.
• Repeat until no more pairs can be found.

Finding the lowest pair of expressions

SAT by itself does not try to find the lowest solution, nor does the CEGIS-loop built on top of it. But we can use the CEGIS-loop as an oracle to answer the question: is there any solution in (a restricted part of) the search space, and that lets us do a bitwise binary search - the "find one bit at the time" variant of binary search, typically discussed in a completely different context. Bitwise binary search maps well to SAT, not directly of course, but in the following way:

1. Initialize the prefix to an empty list.
2. If the prefix has the same size as a pair of expressions, directly return the result of the CEGIS-oracle.
3. Extend the prefix with false.
4. Ask the CEGIS-oracle if there is a solution that starts with the prefix, if there is, go back to step 2.
5. Otherwise, turn the false at the end of the prefix into true, then go back to step 2.

Asking a SAT solver for a solution that starts with a given prefix is easy and tends to make the SAT instance easier to solve (especially when the prefix is long), this only involves forcing some variables (the ones covered by the prefix) to be true or false, using single-literal clauses.

A very useful optimization can be done in step 4: when the CEGIS-oracle says there is a solution with the current prefix, the solution may have some extra zeroes after the prefix which we can use directly to extend the prefix for free. That saves a lot of SAT solves when the solutions tend to have a lot of zeroes in them, which in my case they do, due to extensive use of one-hot encoding.

Encoding the lower bound

There is a neat way to encode that an integer must be greater-than-or-equal-to some given constant, which I haven't seen people talk about (perhaps it's part of the folklore?), using only popcnt(lower_bound) clauses. The idea here is that for every bit that's set in the bound, at least one of the following bits must be set in the solution: that bit itself, or any more-significant bit that is zero in the lower bound. That only takes one clause to encode, a clause containing the variable that corresponds to the set bit, and the variables corresponding to the zeroes to the left of that set bit.

Code

For concreteness, here's how I implemented constraining solutions to conform to the prefix, it's really simple:

// set the prefix (used by binary search)
for (size_t i = 0; i < prefix.size(); i++)
{
    if (prefix[i])
        s.addClause(allprogbits[i]);
    else
        s.addClause(~allprogbits[i]);
}

Here's how I set the lower bound:

// if there is a lower bound (used by binary search), enforce it
if (!lower_bound.empty())
{
    vector<Lit> cl;
    for (size_t i = 0; i < lower_bound.size(); i++) {
        if (lower_bound[i]) {
            cl.push_back(allprogbits[i]);
            s.addClause(cl);
            cl.pop_back();
        }
        else
        {
            cl.push_back(allprogbits[i]);
        }
    }
}

And binary search (with the optimization to keep the extra zeroes that the solver gives for free) looks like this:

optional<pair<vector<InstrB>, vector<InstrB>>> find_lowest(vector<bool>& prefix,
      vector<vector<int>>& inputs)
{
    do {
        if (prefix.size() == progbits) {
            return format_progbits(synthesize(prefix, inputs),
                inputcount, lhs_size, rhs_size);
        }
        else {
            prefix.push_back(false);
            auto f = synthesize(prefix, inputs);
            if (f.has_value()) {
                // if the next bits are already zero in the solution, keep them zero
                auto bits = *f;
                while (prefix.size() < progbits && !bits[prefix.size()])
                    prefix.push_back(false);
                continue;
            }

            prefix.pop_back();
            prefix.push_back(true);
        }
    } while (true);
}

The full code of my implementation of this idea is available on gitlab. It's a bit crap but at least it should show any detail that you may still be wondering about. In the code I also constrain expressions to not be "a funny way to write zero" and to not be "a complicated way to do nothing", otherwise a lot of less-interesting identities would be generated.

Sorting the nibbles of a u64

I was reminded (on mastodon) of this nibble-sorting technique (it could be adapted to other element sizes), which I apparently had only vaguely tweeted about in the past. It deserves a post, so here it is.

Binary LSD radix sort can be expressed as a sequence of stable-partitions, first stable-partitioning based on the least-significant bit, then on the second-to-least-significant bit and so on.

In modern x86, pext essentially implements half of a stable partition, only the half that moves a subset of the elements down towards lower indices. If we do that twice, the second time with an inverted mask, and shift the subset of elements where the mask is set left to put it at the top, we get a gadget that partitions a u64 based on a mask:

(pext(x, mask) << popcount(~mask)) | pext(x, ~mask)

This is sometimes called the sheep-and-goats operation.

For radix sort the masks that we need are, in order, the least significant bit of each element, each broadcasted to cover the whole corresponding element, then the same thing but with the second-to-least-signficant bit and so on. One way to express that is by shifting the whole thing right to put the bit that we want to broadcast in the the least significant position of the element, and then multiplying by 15 to broadcast that bit into every bit of the element. Different compilers handled that multiplication by 15 differently (there are alternative ways to express that).

static ulong sort_nibbles(ulong x)
{
    ulong m = 0x1111111111111111;
    ulong t = (x & m) *15;
    x = (Bmi2.X64.ParallelBitExtract(x, t) << BitOperations.PopCount(~t)) |
        Bmi2.X64.ParallelBitExtract(x, ~t);
    t = ((x >> 1) & m) * 15;
    x = (Bmi2.X64.ParallelBitExtract(x, t) << BitOperations.PopCount(~t)) |
        Bmi2.X64.ParallelBitExtract(x, ~t);
    t = ((x >> 2) & m) * 15;
    x = (Bmi2.X64.ParallelBitExtract(x, t) << BitOperations.PopCount(~t)) |
        Bmi2.X64.ParallelBitExtract(x, ~t);
    t = ((x >> 3) & m) * 15;
    x = (Bmi2.X64.ParallelBitExtract(x, t) << BitOperations.PopCount(~t)) |
        Bmi2.X64.ParallelBitExtract(x, ~t);
    return x;
}

It's easy to extend this to a key-value sort. Hypothetically you could use that key-value sort to invert a permutation (sorting the values 0..15 by the permutation), but you can do much better with AVX512.

Sharpening a lower bound with KnownBits information

I have written about this before, but that was a long time ago, I've had a lot more practice with similar things since then. This topic came up on Mastodon, inspiring me to give it another try. Actually the title is a bit of a lie, I will be using Miné's bitfield domain in which we have bitvectors z indicating the bits that can be zero and o indicating the bits that can be one (as opposed to bitvectors which indicate the bits known to be zero and the bits known to be one respectively, or bitvectors ⁰b and ¹b as used in the paper linked below where ¹b has bits set that are known to be set and ⁰b has bits unset that are known to be unset). The exact representation doesn't really matter.

The problem, to be clear, is that suppose we have a lower bound on some variable, along with some knowledge about its bits (knowing that some bits have a fixed value, which others do not), for example we may know that a variable is even (its least significant bit is known to be zero) and at least 5. "Sharpening" the lower bound means increasing it, if possible, so that the lower bound "fits" the knowledge we have about the bits. If a value is even and at least 5, it is also at least 6, so we can increase the lower bound.

As a more recent reference for an algorithm that is better than my old one, you can read Sharpening Constraint Programming approaches for Bit-Vector Theory.

As that paper notes, we need to find the highest bit in the current lower bound that doesn't "fit" the KnownBits (or z, o pair from the bitfield domain) information, and then either:

• If that bit was not set but should be, we need to set it, and reset any lower bits that are not required to be set (lower bound must go up, but only as little as possible).
• If that bit was set but shouldn't be, we need to reset it, and in order to do that we need to set a higher bit that wasn't set yet, and also reset any lower bits that are not required to be set.

So far so good. What that paper doesn't tell you, is that these are essentially the same case, and we can do:

• Starting from the highest "wrong" bit in the lower bound, find the lowest bit that is unset but could be set, set it, and clear any lower bits that are not required to be set.

That mostly sounds like the second case, what allows the original two cases to be unified is the fact that the bit we find is the same as the bit that needs to be set in the first case too.

As a reminder, x & -x is a common technique used to extract or isolate the lowest set bit aka blsi. It can also be written as x & (~x + 1), and if we change the 1 to some other constant, we can use this technique to find the lowest set bit but starting from some position that is not necessarily the least significant bit. So if we start from highestSetBit(~low & o), we find the bit we're looking for. Actually the annoying part is highestSetBit. Putting the rest together, we may get an implementation like this:

uint64_t sharpen_low(uint64_t low, uint64_t z, uint64_t o)
{
    uint64_t m = (~low & ~z) | (low & ~o);
    if (m) {
        uint64_t target = ~low & o;
        target &= ~target + highestSetBit(m);
        low = (low & -target) | target;
        low |= ~z;
    }
    return low;
}

The branch on m is a bit annoying, but on the plus side it means that the input of highestSetBit is always non-zero. Zero is otherwise a bit of an annoying case to handle in highestSetBit. In modern C++, you can use std::bit_floor for highestSetBit.

Sharpening the upper bound is symmetric, it can be implemented as ~sharpen_low(~high, o, z) or you could push the bitwise flips "inside" the algorithm and do some algebra to cancel them out.

Multiplying 64x64 bit-matrices with GF2P8AFFINEQB

This is a relatively simple use of GF2P8AFFINEQB. By itself GF2P8AFFINEQB essentially multiplies two 8x8 bit-matrices (but with transpose-flip applied to the second operand, and an extra XOR by a byte that we can set to zero). A 64x64 matrix can be seen as a block-matrix where each block is an 8x8 matrix. You can also view this as taking the ring of 8x8 matrices over GF(2), and then working with 8x8 matrices with elements from that ring. All we really need to do is write an 8x8 matrix multiplication and let GF2P8AFFINEQB take care of the complicated part.

Using the "full" 512-bit version of VGF2P8AFFINEQB from AVX-512, one VGF2P8AFFINEQB instructions performs 8 of those products. A convenient way to use them is by broadcasting an element (which is really an 8x8 bit-matrix but let's put that aside for now) from the left matrix to all QWORD elements of a ZMM vector, and multiplying that by a row of the right matrix. That way we end up with a row of the result, which is nice to work with: no reshaping or horizontal-SIMD required. XOR-ing QWORDs together horizontally could be done relatively reasonably with another GF2P8AFFINEQB trick, which is neat but avoiding it is even better. All we need to do to compute a row of the result (still viewed as an 8x8 matrix) is 8 broadcasts, 8 VGF2P8AFFINEQB, and XOR-ing the 8 results together, which doesn't take 8 VPXORQ because VPTERNLOGQ can XOR three vectors together. Then just do this for each row of the result.

There are two things that I've skipped so far. First, the built-in transpose-flip of GF2P8AFFINEQB needs to be cancelled out with a flip-transpose (unless explicitly working with a matrix in a weird format is OK). Second, working with an 8x8 block-matrix is mathematically "free" by imagining some dotted lines running through the matrix, but in order to get the right data into GF2P8AFFINEQB we have to actually rearrange it (again: unless the weird format is OK).

One way to implement a flip-transpose (ie the inverse of the bit-permutation that GF2P8AFFINEQB applies to its second operand) is by reversing the bytes in each QWORD and then left-multiplying (in the second of GF2P8AFFINEQB-ing with constant as the first operand) by a flipped identity matrix, which as a QWORD looks like: 0x0102040810204080. Reversing the bytes in each QWORD could be done with a VPERMB, there are other ways, but we're about to have a VPERMB anyway.

Rearranging the data between a fully row-major layout and an 8x8 matrix in which each element is an 8x8 bit-matrix is easy, that's just an 8x8 transpose after all, so just VPERMB. That's needed both for the inputs and the output. The input that is the right-hand operand of the overall matrix multiplication also needs to have a byte-reverse applied to each QWORD, the same VPERMB that does that transpose can also do that byte-reverse.

Here's one way to put that all together:

array<uint64_t, 64> mmul_gf2_avx512(const array<uint64_t, 64>& A, const array<uint64_t, 64>& B)
{
    __m512i id = _mm512_set1_epi64(0x0102040810204080);
    __m512i tp = _mm512_setr_epi8(
        0, 8, 16, 24, 32, 40, 48, 56,
        1, 9, 17, 25, 33, 41, 49, 57,
        2, 10, 18, 26, 34, 42, 50, 58,
        3, 11, 19, 27, 35, 43, 51, 59,
        4, 12, 20, 28, 36, 44, 52, 60,
        5, 13, 21, 29, 37, 45, 53, 61,
        6, 14, 22, 30, 38, 46, 54, 62,
        7, 15, 23, 31, 39, 47, 55, 63);
    __m512i tpr = _mm512_setr_epi8(
        56, 48, 40, 32, 24, 16, 8, 0,
        57, 49, 41, 33, 25, 17, 9, 1,
        58, 50, 42, 34, 26, 18, 10, 2,
        59, 51, 43, 35, 27, 19, 11, 3,
        60, 52, 44, 36, 28, 20, 12, 4,
        61, 53, 45, 37, 29, 21, 13, 5,
        62, 54, 46, 38, 30, 22, 14, 6,
        63, 55, 47, 39, 31, 23, 15, 7);
    array<uint64_t, 64> res;

    __m512i b_0 = _mm512_permutexvar_epi8(tpr, _mm512_loadu_epi64(&B[0]));
    __m512i b_1 = _mm512_permutexvar_epi8(tpr, _mm512_loadu_epi64(&B[8]));
    __m512i b_2 = _mm512_permutexvar_epi8(tpr, _mm512_loadu_epi64(&B[16]));
    __m512i b_3 = _mm512_permutexvar_epi8(tpr, _mm512_loadu_epi64(&B[24]));
    __m512i b_4 = _mm512_permutexvar_epi8(tpr, _mm512_loadu_epi64(&B[32]));
    __m512i b_5 = _mm512_permutexvar_epi8(tpr, _mm512_loadu_epi64(&B[40]));
    __m512i b_6 = _mm512_permutexvar_epi8(tpr, _mm512_loadu_epi64(&B[48]));
    __m512i b_7 = _mm512_permutexvar_epi8(tpr, _mm512_loadu_epi64(&B[56]));
    
    b_0 = _mm512_gf2p8affine_epi64_epi8(id, b_0, 0);
    b_1 = _mm512_gf2p8affine_epi64_epi8(id, b_1, 0);
    b_2 = _mm512_gf2p8affine_epi64_epi8(id, b_2, 0);
    b_3 = _mm512_gf2p8affine_epi64_epi8(id, b_3, 0);
    b_4 = _mm512_gf2p8affine_epi64_epi8(id, b_4, 0);
    b_5 = _mm512_gf2p8affine_epi64_epi8(id, b_5, 0);
    b_6 = _mm512_gf2p8affine_epi64_epi8(id, b_6, 0);
    b_7 = _mm512_gf2p8affine_epi64_epi8(id, b_7, 0);

    for (size_t i = 0; i < 8; i++)
    {
        __m512i a_tiles = _mm512_loadu_epi64(&A[i * 8]);
        a_tiles = _mm512_permutexvar_epi8(tp, a_tiles);
        __m512i row = _mm512_ternarylogic_epi64(
            _mm512_gf2p8affine_epi64_epi8(_mm512_permutexvar_epi64(_mm512_set1_epi64(0), a_tiles), b_0, 0),
            _mm512_gf2p8affine_epi64_epi8(_mm512_permutexvar_epi64(_mm512_set1_epi64(1), a_tiles), b_1, 0),
            _mm512_gf2p8affine_epi64_epi8(_mm512_permutexvar_epi64(_mm512_set1_epi64(2), a_tiles), b_2, 0), 0x96);
        row = _mm512_ternarylogic_epi64(row,
            _mm512_gf2p8affine_epi64_epi8(_mm512_permutexvar_epi64(_mm512_set1_epi64(3), a_tiles), b_3, 0),
            _mm512_gf2p8affine_epi64_epi8(_mm512_permutexvar_epi64(_mm512_set1_epi64(4), a_tiles), b_4, 0), 0x96);
        row = _mm512_ternarylogic_epi64(row,
            _mm512_gf2p8affine_epi64_epi8(_mm512_permutexvar_epi64(_mm512_set1_epi64(5), a_tiles), b_5, 0),
            _mm512_gf2p8affine_epi64_epi8(_mm512_permutexvar_epi64(_mm512_set1_epi64(6), a_tiles), b_6, 0), 0x96);
        row = _mm512_xor_epi64(row,
            _mm512_gf2p8affine_epi64_epi8(_mm512_permutexvar_epi64(_mm512_set1_epi64(7), a_tiles), b_7, 0));
        row = _mm512_permutexvar_epi8(tp, row);
        _mm512_storeu_epi64(&res[i * 8], row);
    }
    
    return res;
}

When performing multiple matrix multiplications in a row, it may make sense to leave the intermediate results in the format of an 8x8 matrix of 8x8 bit-matrices. The B matrix needs to be permuted anyway, but the two _mm512_permutexvar_epi8 in the loop can be removed. And obviously, if the same matrix is used as the B matrix several times, it only needs to be permuted once. You may need to manually inline the code to convince your compiler to keep the matrix in registers.

Crude benchmarks

A very boring conventional implementation of this 64x64 matrix multiplication may look like this:

array<uint64_t, 64> mmul_gf2_scalar(const array<uint64_t, 64>& A, const array<uint64_t, 64>& B)
{
    array<uint64_t, 64> res;
    for (size_t i = 0; i < 64; i++) {
        uint64_t result_row = 0;
        for (size_t j = 0; j < 64; j++) {
            if (A[i] & (1ULL << j))
                result_row ^= B[j];
        }
        res[i] = result_row;
    }
    return res;
}

There are various ways to write this slightly differently, some of which may be a bit faster, that's not really the point.

On my PC, which has a 11600K (Rocket Lake) in it, mmul_gf2_scalar runs around 500 times as slow (in terms of the time taken to perform a chain of dependent multiplications) as the AVX-512 implementation. Really, it's that slow - but that is partly due to my choice of data: I mainly benchmarked this on random matrices where each bit has a 50% chance of being set. The AVX-512 implementation does not care about that at all, while the above scalar implementation has thousands (literally) of branch mispredictions. That can be fixed without using SIMD, for example:

array<uint64_t, 64> mmul_gf2_branchfree(const array<uint64_t, 64>& A, const array<uint64_t, 64>& B)
{
    array<uint64_t, 64> res;
    for (size_t i = 0; i < 64; i++) {
        uint64_t result_row = 0;
        for (size_t j = 0; j < 64; j++) {
            result_row ^= B[j] & -((A[i] >> j) & 1);
        }
        res[i] = result_row;
    }
    return res;
}

That was, in my benchmark, already about 8 times as fast as the branching version, if I don't let MSVC auto-vectorize. If I do let it auto-vectorize (with AVX-512), this implementation becomes 25 times as fast as the branching version. This was not supposed to be a post about branch (mis)prediction, but be careful out there, you might get snagged on a branch.

It would be interesting to compare the AVX-512 version against established finite-field (or GF(2)-specific) linear algebra packages, such as FFLAS-FFPACK and M4RI. Actually I tried to include M4RI in the benchmark, but it ended up being 10 times as slow as mmul_gf2_branchfree (when it is auto-vectorized). That's bizarrely bad so I probably did something wrong, but I've already sunk 4 times as much time into getting M4RI to work at all as it took to write the code which this blog post is really about, so I'll just accept that I did it wrong and leave it at that.

Implementing grevmul with GF2P8AFFINEQB

As a reminder, grev is generalized bit-reversal, and performs a bit-permutation that corresponds to XORing the indices of the bits of the left operand by the number given in the right operand. A possible (not very suitable for actual use, but illustrative) implementation of grev is:

def grev(bits, k):
    return bits[np.arange(len(bits)) ^ k]

grevmul (see also this older post where I list some of its properties) can be defined in terms of grev (hence the name^[1]), with grev replacing the left shift in a carryless multiplication. But let's do something else first. An interesting way to look at multiplication (plain old multiplication between natural numbers) is as:

1. Form the Cartesian product of the inputs, viewed as arrays of bits.
2. For each pair in the Cartesian product, compute the AND of the two bits.
3. Send the AND of each pair with index (i, j) to the bin i + j, to be accumulated with the function (+).
4. Resolve the carries.

Carryless multiplication works mostly the same way except that accumulation is done with XOR, which makes step 4 unnecessary. grevmul also works mostly the same way, with accumulation also done with XOR, but now the pair with index (i, j) is sent to the bin i XOR j.

	+	^
+	imul	clmul
^	???	grevmul

That won't be important for the implementation, but it may help you think about what grevmul does, and this will be on the test. OK there is no test, but you can test yourself by explaining why (popcnt(a & b) & 1) == (grevmul(a, b) & 1), based on reasoning about the Cartesian-product-algorithm for grevmul.

Implementing grevmul with GF2P8AFFINEQB

Some of you may have already seen a version of the code that I am about to discuss, although I have made some changes since. Nothing serious, but while thinking about how the code worked, I encountered some opportunities to make polish it up.

GF2P8AFFINEQB computes, for each QWORD (so for now, let's concentrate on one QWORD of the result), the product of two bit-matrices, where the left matrix comes from the first input and the right matrix is the transpose-flip (or flip-transpose, however you prefer to think about it) of the second input. You can think of it as a mutant of the mxor operation found in Knuth's MMIX instruction set, and from here on I will use the name bmatxor for the version of this operation that simply multiplies two 8x8 bit-matrices, with none of that transpose-flip business^[2]. There is also a free XOR by a constant byte thrown in at the end, which can be safely ignored by setting it to zero. The transpose-flip however need to be worked around or otherwise taken into account. You may also want to read (Ab)using gf2p8affineqb to turn indices into bits first for some extra familiarity with / alternate view of GF2P8AFFINEQB.

To implement grevmul(a, b), we need some linear combination (controlled by b) of permuted (by grev) versions of a (the roles of a and b can be swapped since grevmul is commutative, which the Cartesian product-based algorithm makes clear). GF2P8AFFINEQB is all about linear combinations, but it works with 8x8 matrices, not 64x64 (put that in AVX-4096) which would have made it really simple. Fortunately, we can slice a grevmul however we want.

Now to start in the middle, let's say we have 8 copies of a byte (a byte of b), with the i'th copy grev'ed by i, concatenated into a QWORD that I will call m. bmatxor(a, m) would (thinking of a matrix multiplication AB as forming linear combinations of rows of B), for each row of the result, form a linear combination (controlled by a) of grev'ed copies of the byte from b. That may seem like it would be wrong, since every byte of a is done separately and uses the same m, so it's "missing" a grev by 8 for the second byte, 16 for the third byte, etc. But if x is a byte (not if and only if, just regular "if"), then grev(x, 8 * i) is the same as x << (8 * i) and the second byte is indeed already in the second position anyway, so we get this for free. Thus, mxor(a, m) would allow us to grevmul a 64-bit number by an 8-bit number. If we could just do that 8 times (for each byte of b) and combine the results, we're done.

But we don't have bmatxor, we have GF2P8AFFINEQB with its built-in transpose-flip, and that presents a choice: either put another GF2P8AFFINEQB before, or after, the "main" GF2P8AFFINEQB to counter that built-in transpose. Not the whole transpose-flip, let's put the flip aside for now. There is a small reason to favour the "extra GF2P8AFFINEQB after the main one" order, namely that that results in GF2P8AFFINEQB(m, broadcast(a)) (as opposed to GF2P8AFFINEQB(broadcast(a), m)) and when a comes from memory it can be loaded and broadcasted directly with a {1to8} broadcasted memory operand. That option would not be available if a was the first operand of the GF2P8AFFINEQB. This is a small matter, but we have to make the choice somehow, and there seems to be no difference aside from this.

At this point there are two (and a half) pieces of the puzzle left: forming m, and horizontally combining 8 results.

Forming m

If we would first form 8 copies of a byte of b and then try to grev those by their respective indices, that would be hard. But doing it the other way around is easy, broadcast b into each QWORD of a 512-bit vector, grev each QWORD by its index, then transpose the whole vector as an 8x8 matrix of bytes. Actually for constant-reuse (loading fewer humongous vector constants is rarely bad) and because of the built-in transpose-flip it turns out slightly better to transpose-flip that 8x8 matrix of bytes as well, that doesn't cost any more than just transposing it.

grev-ing each QWORD by its index is easy, perhaps easier than it sounds. A grev by 0..7 only rearranges the bits within each byte, which is easy to do with a single GF2P8AFFINEQB with a constant as the second operand (a "P" step).

An 8x8 transpose-flip is just a VPERMB by some index vector.^[3]

Combining the results

After the "middle" step, if we went with the "extra GF2P8AFFINEQB before the main GF2P8AFFINEQB"-route, we would have bmatxor(a, m) in each QWORD (with a different m per QWORD), which need to be combined. If we were implementing a plain old integer multiplication, the value in the QWORD with the index i would be shifted left by 8 * i and then all of them would be summed up. Since we're implementing a grevmul, that value is grev'ed by 8 * i (which is just some byte permutation) and the resulting QWORDs are XORed.

If we go with the "extra GF2P8AFFINEQB after the main GF2P8AFFINEQB"-route, which I chose, then there is a GF2P8AFFINEQB to do before we can start combining QWORDs. We really only need it to un-transpose the result of the "main" GF2P8AFFINEQB, the rest is just a byte-permutation and we're about to do a VPERMB anyway (if we choose the cool way of XORing the QWORDs together), but there is a neat opportunity here: if we re-use the same set of bit-matrices that was used to grev b by 0..7, in addition to the transpose that we wanted we also permute the bytes such that we grev each QWORD by 8 * i as a bonus.

Now we could just XOR-fold the vector 3 times to end up with the XOR of all eight QWORDs, and that would be a totally valid implementation, but it would also be boring. Alternatively, if we transpose the vector as an 8x8 matrix of bytes again (it can be a transpose-flip, so we get to reuse the same index vector as before), then the i'th byte of each QWORD would be gathered in the i'th QWORD of the transpose and bmatxor(0xFF, vector) would XOR together all bytes with the same index and give us a vector that has one byte of the final result per QWORD, easily extractable with VPMOVQB. We still don't have bmatxor though, we have bmatxor with an extra transpose-flip, which can be countered the usual way, with yet another GF2P8AFFINEQB.

As yet another alternative^[4], after similar transpose trickery bmatxor(vector, 0xFF) would also XOR together bytes with the same index but leave the result in a form that can be extracted with VPMOVB2M, which puts the result in a mask register but it's not too bad to move it from there to a GPR.

The code

In case anyone makes it this far, here is one possible embodiment^[5] of the algorithm described herein:

uint64_t grevmul_avx512(uint64_t a, uint64_t b)
{
    uint64_t id = 0x0102040810204080;
    __m512i grev_by_index = _mm512_setr_epi64(
        id,
        grev(id, 1),
        grev(id, 2),
        grev(id, 3),
        grev(id, 4),
        grev(id, 5),
        grev(id, 6),
        grev(id, 7));
    __m512i tp_flip = _mm512_setr_epi8(
        56, 48, 40, 32, 24, 16, 8, 0,
        57, 49, 41, 33, 25, 17, 9, 1,
        58, 50, 42, 34, 26, 18, 10, 2,
        59, 51, 43, 35, 27, 19, 11, 3,
        60, 52, 44, 36, 28, 20, 12, 4,
        61, 53, 45, 37, 29, 21, 13, 5,
        62, 54, 46, 38, 30, 22, 14, 6,
        63, 55, 47, 39, 31, 23, 15, 7);
    __m512i m = _mm512_set1_epi64(b);
    m = _mm512_gf2p8affine_epi64_epi8(m, grev_by_index, 0);
    m = _mm512_permutexvar_epi8(tp_flip, m);
    __m512i t512 = _mm512_gf2p8affine_epi64_epi8(m, _mm512_set1_epi64(a), 0);
    t512 = _mm512_gf2p8affine_epi64_epi8(grev_by_index, t512, 0);
    t512 = _mm512_permutexvar_epi8(tp_flip, t512);
    t512 = _mm512_gf2p8affine_epi64_epi8(_mm512_set1_epi64(0x8040201008040201), t512, 0);
    t512 = _mm512_gf2p8affine_epi64_epi8(t512, _mm512_set1_epi64(0xFF), 0);
    return _mm512_movepi8_mask(t512);
}

The end

As far as I know, no one really uses grevmul for anything, so being able to compute it somewhat efficiently (more efficiently than a naive scalar solution at least) is not immediately useful. On the other hand, if an operation is not known to be efficiently computable, that may preclude its use. But the point of this post is more to show something neat.

Originally I had found the sequence of _mm512_gf2p8affine_epi64_epi8(m, _mm512_set1_epi64(a), 0) and _mm512_gf2p8affine_epi64_epi8(grev_by_index, t512, 0) as a solution to grevmul-ing a QWORD by a (constant) byte, using a SAT solver (shout-out to togasat for being easy to add to a project - though admittedly I eventually bit the bullet and switched to MiniSAT). That formed the starting point of this investigation / puzzle-solving session. It may be possible to pull a more complete solution out of the void with a SAT/SMT based technique such as CEGIS, perhaps the bitwise-bilinear nature of grevmul can be exploited (I used the bitwise-linear nature of grevmul-by-a-constant in my SAT-experiments to represent the problem as a composition of matrices over GF(2)).

Almost half of the steps of this algorithm are some kind of transpose, which has also been the case with some other SIMD algorithms that I recently had a hand in. I used to think of a transpose as "not really doing anything", barely worth the notation when doing linear algebra, but maybe I was wrong.

---

[1] Maybe it's less "the name" and more "what I decided to call it". I'm not aware of any established name for this operation.
[2] This makes it sound more negative than it really is. The transpose-flip often needs to be worked around when we don't want it, but that's not that bad. Having no easy access to a transpose would be much worse to work around when we do need it. Separate bmatxor and bmattranspose instructions would have been nice.
[3] GF2P8AFFINEQB trickery is nice, but when I recently wrote some AVX2 code it was VPERMB that I missed the most.
[4] Can we stop with the alternatives and just pick something?
[5] No patents were read in the development of this algorithm, nor in the writing of this blog post.

Enumerating all mathematical identities (in fixed-size bitvector arithmetic with a restricted set of operations) of a certain size

Once again a boring-but-specific title, I don't want to clickbait the audience after all. Even so, let's get some "disclaimers" out of the way.

• "All" includes both boring and interesting identities. I tried to remove the most boring ones, so it's no longer truly all identities, but still a lot of boring ones remain. The way I see it, the biggest problem which the approach that I describe in this blog post has, is generating too much "true but boring" junk.
• This approach is, as far as I know, absolutely limited to fixed-size bitvectors, but that's what I'm interested in anyway. To keep things reasonable, the size should be small, which does result in some differences with eg the arithmetic of 64-bit bitvectors. Most of the results either directly transfer to larger sizes, or generalize to larger sizes.
• The set of operations is restricted to those that are cheap to implement in CNF SAT, that is not a hard limitation but a practical one.
• "Of a certain size" means we have to pick in advance the number of operations on both sides of the mathematical identity, and then only identities with exactly that number of operations (counted in the underlying representation, which may represent a seemingly larger expression if the result of an operation is used more than once) are found. This can be repeated for any size we're interested in, but this approach is not very scalable and tends to run out of memory if there are too many identities of the requested size.

The results look something like this. Let's say we want "all" identities that involve 2 variables, 2 operations on the left, and 0 operations (ie only a variable) on the right. The result would be the following, keep in mind that a bunch of redundant and boring identities are filtered out.

(a - (a - b)) == b
(a + (b - a)) == b
((a + b) - a) == b
(b | (a & b)) == b
(a ^ (a ^ b)) == b
(b & (a | b)) == b
// Done in 0s. Used a set of 3 inputs.

Nothing too interesting so far, but then we didn't ask for much. Here are a couple of selected "more interesting" (but not by any means new or unknown) identities that this approach can also enumerate:

((a & b) + (a | b)) == (a + b)
((a | b) - (a & b)) == (a ^ b)
((a ^ b) | (a & b)) == (a | b)
((a & b) ^ (a | b)) == (a ^ b)
((~ a) - (~ b)) == (b - a)
(~ (b + (~ a))) == (a - b)
  

Now that the expectations have been set accurately (hopefully), let's get into what the approach is.

The Approach

The core mechanism I use is CounterExample-Guided Inductive Synthesis (CEGIS) based on a SAT solver. Glucose worked well, other solvers can be used. Rather than asking CEGIS to generate a snippet of code that performs some specific task however, I ask it to generate two snippets that are equivalent. That does not fundementally change how it operates, which is still a loop of:

1. Synthesize code that does the right thing for each input in the set of inputs to check.
2. Check whether the code matches the specification. If it does, we're done. If it doesn't, add the counter-example to the set of inputs to check.

Both synthesis and checking could be performed by a SAT solver, but I only use a SAT solver for synthesis. For checking, since 4-bit bitvectors have so few combinations, I just brute force every possible valuation of the variables.

When a pair of equivalent expressions has been found, I add its negation as a single clause to prevent the same thing from being synthesized again. This is what enables pulling out one identity after the other. In my imagination, that looks like Thor smashing his mug and asking for another.

Solving for programs may seem odd, the trick here is to represent a program as a sequence of instructions that are constructed out of boolean variables, the SAT solver is then invoked to solve for those variables.

The code is available on gitlab.

Original motivation

The examples I gave earlier only involve "normal" bitvector arithmetic. Originally what I set out to do is discover what sorts of mathematical identities are true in the context of trapping arithmetic (in which subtraction and addition trap on signed overflow), using the rule that two expressions are equivalent if and only if they have the same behaviour, in the following sense: for all valuations of the variables, the two expressions either yield the same value, or they both trap. That rule is also implemented in the linked source.

Many of the identities found in that context involve a trapping operation that can never actually trap. For example the trapping subtraction (the t-suffix in -t indicates that it is the trapping version of subtraction) in (b & (~ a)) == (b -t (a & b)) cannot trap (boring to prove so I won't bother). But the "infamous" (among who, maybe just me) (-t (-t (-t a))) == (-t a) is also enumerated and the sole remaining negation can trap but does so in exactly the same case as the original three-negations-in-a-row (namely when a is the bitvector with only its sign-bit set). Here is a small selection of nice identities that hold in trapping arithmetic:

((a & b) +t (a | b)) == (a +t b)
((a | b) -t (a & b)) == (a ^ b)
((~ b) -t (~ a)) == (a -t b)
(~ (b +t (~ a))) == (a -t b)
(a -t (a -t b)) == (a - (a -t b))  // note: one of the subtractions is a non-trapping subtraction

Future directions

A large source of boring identities is the fact that if f(x) == g(x), then also f(x) + x == g(x) + x and f(x) & x == g(x) & x and so on, which causes "small" identities to show up again as part of larger ones, without introducing any new information, and multiplied in myriad ways. If there was a good way to prevent them from being enumerated (it would have to be sufficiently easy to state in terms of CNF SAT clauses, to prevent slowing down the solver too much), or to summarize the full output, that could make the output of the enumeration more human-digestible.

There is a part 2 for this post.

The solutions to 𝚙𝚘𝚙𝚌𝚗𝚝(𝚡) < 𝚝𝚣𝚌𝚗𝚝(𝚡) and why there are Fibonacci[n] of them below 2ⁿ

popcnt(x) < tzcnt(x) asks the question "does x have fewer set bits than it has trailing zeroes". It's a simple question with a simple answer, but cute enough to think about on a Sunday morning.[1]

Here are the solutions for 8 bits, in order: 0, 4, 8, 16, 24, 32, 40, 48, 64, 72, 80, 96, 112, 128, 136, 144, 160, 176, 192, 208, 224[2]

In case you find decimal hard to do read (as I do), here they are again in binary: 00000000, 00000100, 00001000, 00010000, 00011000, 00100000, 00101000, 00110000, 01000000, 01001000, 01010000, 01100000, 01110000, 10000000, 10001000, 10010000, 10100000, 10110000, 11000000, 11010000, 11100000

Simply staring at the values doesn't do much for me. To get a better handle on what's going on, let's recursively (de-)construct the set of n-bit solutions.

The most significant bit of an n-bit solution is either 0 or 1:

0. If it is 0, then that bit affects neither the popcnt nor the tzcnt so removing it must yield an (n-1)-bit solution.
1. If it is 1, then removing it along with the least significant bit (which must be zero, there are no odd solutions since their tzcnt would be zero) would decrease the both popcnt and the tzcnt by 1, yielding an (n-2)-bit solution.

This "deconstructive" recursion is slightly awkward. The constructive version would be: you can take the (n-1)-bit solutions and prepend a zero to them, and you can take the (n-2)-bit solutions and prepend a one and append a zero to them. However, it is less clear then (to me anyway) that those are the only n-bit solutions. The "deconstructive" version starts with all n-bit solutions and splits them into two obviously-disjoint groups, removing the possibility of solutions getting lost or being counted double.

The F(n) = F(n - 1) + F(n - 2) structure of the number of solutions is clear, but there are different sequences that follow that same recurrence that differ in their base cases. Here we have 1 solution for 1-bit integers (namely zero) and 1 solution for 2-bit integers (also zero), so the base cases are 1 and 1 as in the Fibonacci sequence.

This is probably all useless, and it's barely even bitmath.

---

[1] Or whenever, but it happens to be a Sunday morning for me right now.
[2] This sequence does not seem to be on the OEIS at the time of writing.

Partial sums of popcount

The partial sums of popcount, aka A000788: Total number of 1's in binary expansions of 0, ..., n can be computed fairly efficiently with some mysterious code found through its OEIS entry (see the link Fast C++ function for computing a(n)): (reformatted slightly to reduce width)

unsigned A000788(unsigned n)
{
    unsigned v = 0;
    for (unsigned bit = 1; bit <= n; bit <<= 1)
        v += ((n>>1)&~(bit-1)) +
             ((n&bit) ? (n&((bit<<1)-1))-(bit-1) : 0);
    return v;
}

Knowing what we (or I, anyway) know from computing the partial sums of blsi and blsmsk, let's try to improve on that code. "Improve" is a vague goal, let's say we don't want to loop over the bits, but also not just unroll by 64x to do this for a 64-bit integer.

First let's split this thing into the sum of an easy problem and a harder problem, the easy problem being the sum of (n>>1)&~(bit-1) (reminder that ~(bit-1) == -bit, unsigned negation is safe, UB-free, and does exactly what we need, even on hypothetical non-two's-complement hardware). This is the same thing we saw in the partial sum of blsi, bit k of n occurs k times in the sum, which we can evaluate like this:

uint64_t v = 
    ((n & 0xAAAA'AAAA'AAAA'AAAA) >> 1) +
    ((n & 0xCCCC'CCCC'CCCC'CCCC) << 0) +
    ((n & 0xF0F0'F0F0'F0F0'F0F0) << 1) +
    ((n & 0xFF00'FF00'FF00'FF00) << 2) +
    ((n & 0xFFFF'0000'FFFF'0000) << 3) +
    ((n & 0xFFFF'FFFF'0000'0000) << 4);

The harder problem, the contribution from ((n&bit) ? (n&((bit<<1)-1))-(bit-1) : 0), has a similar pattern but more annoying in three ways. Here's an example of the pattern, starting with n in the first row and listing the values being added together below the horizontal line:

00100011000111111100001010101111
--------------------------------
00000000000000000000000000000001
00000000000000000000000000000010
00000000000000000000000000000100
00000000000000000000000000001000
00000000000000000000000000010000
00000000000000000000000000110000
00000000000000000000000010110000
00000000000000000000001010110000
00000000000000000100001010110000
00000000000000001100001010110000
00000000000000011100001010110000
00000000000000111100001010110000
00000000000001111100001010110000
00000000000011111100001010110000
00000000000111111100001010110000
00000001000111111100001010110000
00000011000111111100001010110000

1. Some anomalous thing happens for the contiguous group of rightmost set bits.
2. The weights are based not on the column index, but sort of dynamic based on the number of set bits ...
3. ... to the left of the bit we're looking at. That's significant, "to the right" would have been a lot nicer to deal with.

For problem 1, I'm just going to state without proof that we can add 1 to n and ignore the problem, as long as we add n & ~(n + 1) to the final sum. Problems 2 and 3 are more interesting. If we had problem 2 but counting the bits to the right of the bit we're looking at, that would have nice and easy, instead of (n & 0xAAAA'AAAA'AAAA'AAAA) we would have _pdep_u64(0xAAAA'AAAA'AAAA'AAAA, n), problem solved. If we had a "pdep but from left to right" (aka expand_left) named _pdepl_u64 we could have done this:

uint64_t u = 
    ((_pdepl_u64(0x5555'5555'5555'5555, m) >> shift) << 0) +
    ((_pdepl_u64(0x3333'3333'3333'3333, m) >> shift) << 1) +
    ((_pdepl_u64(0x0F0F'0F0F'0F0F'0F0F, m) >> shift) << 2) +
    ((_pdepl_u64(0x00FF'00FF'00FF'00FF, m) >> shift) << 3) +
    ((_pdepl_u64(0x0000'FFFF'0000'FFFF, m) >> shift) << 4) +
    ((_pdepl_u64(0x0000'0000'FFFF'FFFF, m) >> shift) << 5);

But as far as I know, that requires bit-reversing the inputs (see the update below) of a normal _pdep_u64 and bit-reversing the result, which is not so nice at least on current x64 hardware. Every ISA should have a Generalized Reverse operation like the grevi instruction which used to be in the drafts of the RISC-V Bitmanip Extension prior to version 1.

Update:

It turned out there is a reasonable way to implement _pdepl_u64(v, m) in plain scalar code after all, namely as _pdep_u64(v >> (std::popcount(~m) & 63), m). The & 63 isn't meaningful, it's just to prevent UB at the C++ level.

This approach turned out to be more efficient than the AVX512 approach, so that's obsolete now, but maybe still interesting to borrow ideas from. Here's the scalar implementation in full:

uint64_t _pdepl_u64(uint64_t v, uint64_t m)
{
    return _pdep_u64(v >> (std::popcount(~m) & 63), m);
}

uint64_t partialSumOfPopcnt(uint64_t n)
{
    uint64_t v =
        ((n & 0xAAAA'AAAA'AAAA'AAAA) >> 1) +
        ((n & 0xCCCC'CCCC'CCCC'CCCC) << 0) +
        ((n & 0xF0F0'F0F0'F0F0'F0F0) << 1) +
        ((n & 0xFF00'FF00'FF00'FF00) << 2) +
        ((n & 0xFFFF'0000'FFFF'0000) << 3) +
        ((n & 0xFFFF'FFFF'0000'0000) << 4);
    uint64_t m = n + 1;
    int shift = std::countl_zero(m);
    m = m << shift;
    uint64_t u =
        ((_pdepl_u64(0x5555'5555'5555'5555, m) >> shift) << 0) +
        ((_pdepl_u64(0x3333'3333'3333'3333, m) >> shift) << 1) +
        ((_pdepl_u64(0x0F0F'0F0F'0F0F'0F0F, m) >> shift) << 2) +
        ((_pdepl_u64(0x00FF'00FF'00FF'00FF, m) >> shift) << 3) +
        ((_pdepl_u64(0x0000'FFFF'0000'FFFF, m) >> shift) << 4) +
        ((_pdepl_u64(0x0000'0000'FFFF'FFFF, m) >> shift) << 5);
    return u + (n & ~(n + 1)) + v;
}

Repeatedly calling _pdepl_u64 with the same mask creates some common-subexpressions, they could be manually factored out but compilers do that anyway, even MSVC only uses one actual popcnt instruction (but MSVC, annoyingly, actually performs the meaningless & 63).

Enter AVX512

Using AVX512, we could more easily reverse the bits of a 64-bit integer, there are various ways to do that. But just using that and then going back to scalar pdep would be a waste of a good opportunity to implement the whole thing in AVX512, pdep and all. The trick to doing a pdep in AVX512, if you have several 64-bit integers that you want to pdep with the same mask, is to transpose 8x 64-bit integers into 64x 8-bit integers, use vpexpandb, then transpose back. In this case the first operand of the pdep is a constant, so the first transpose is not necessary. We still have to reverse the mask though. Since vpexpandb takes the mask input in a mask register and we only have one thing to reverse, this trick to bit-permute integers seems like a better fit than Wunk's whole-vector bit-reversal or some variant thereof.

I sort of glossed over the fact that we're supposed to be bit-reversing relative to the most significant set bit in the mask, but that's easy to do by shifting left by std::countl_zero(m) and then doing a normal bit-reverse, so in the end it still comes down to a normal bit-reverse. The result of the pdeps have to be shifted right by the same amount to compensate.

Here's the whole thing: (note that this is less efficient than the updated approach without AVX512)

uint64_t partialSumOfPopcnt(uint64_t n)
{    
    uint64_t v = 
        ((n & 0xAAAA'AAAA'AAAA'AAAA) >> 1) +
        ((n & 0xCCCC'CCCC'CCCC'CCCC) << 0) +
        ((n & 0xF0F0'F0F0'F0F0'F0F0) << 1) +
        ((n & 0xFF00'FF00'FF00'FF00) << 2) +
        ((n & 0xFFFF'0000'FFFF'0000) << 3) +
        ((n & 0xFFFF'FFFF'0000'0000) << 4);
    // 0..63
    __m512i weights = _mm512_setr_epi8(
        0, 1, 2, 3, 4, 5, 6, 7,
        8, 9, 10, 11, 12, 13, 14, 15,
        16, 17, 18, 19, 20, 21, 22, 23,
        24, 25, 26, 27, 28, 29, 30, 31,
        32, 33, 34, 35, 36, 37, 38, 39,
        40, 41, 42, 43, 44, 45, 46, 47,
        48, 49, 50, 51, 52, 53, 54, 55,
        56, 57, 58, 59, 60, 61, 62, 63);
    // 63..0
    __m512i rev = _mm512_set_epi8(
        0, 1, 2, 3, 4, 5, 6, 7,
        8, 9, 10, 11, 12, 13, 14, 15,
        16, 17, 18, 19, 20, 21, 22, 23,
        24, 25, 26, 27, 28, 29, 30, 31,
        32, 33, 34, 35, 36, 37, 38, 39,
        40, 41, 42, 43, 44, 45, 46, 47,
        48, 49, 50, 51, 52, 53, 54, 55,
        56, 57, 58, 59, 60, 61, 62, 63);
    uint64_t m = n + 1;
    // bit-reverse the mask to implement expand-left
    int shift = std::countl_zero(m);
    m = (m << shift);
    __mmask64 revm = _mm512_bitshuffle_epi64_mask(_mm512_set1_epi64(m), rev);
    // the reversal of expand-right with reversed inputs is expand-left
    __m512i leftexpanded = _mm512_permutexvar_epi8(rev, 
        _mm512_mask_expand_epi8(_mm512_setzero_si512(), revm, weights));
    // transpose back to 8x 64-bit integers
    leftexpanded = Transpose64x8(leftexpanded);
    // compensate for having shifted m left
    __m512i masks = _mm512_srlv_epi64(leftexpanded, _mm512_set1_epi64(shift));
    // scale and sum results
    __m512i parts = _mm512_sllv_epi64(masks,
        _mm512_set_epi64(7, 6, 5, 4, 3, 2, 1, 0));
    __m256i parts2 = _mm256_add_epi64(
        _mm512_castsi512_si256(parts), 
        _mm512_extracti64x4_epi64(parts, 1));
    __m128i parts3 = _mm_add_epi64(
        _mm256_castsi256_si128(parts2), 
        _mm256_extracti64x2_epi64(parts2, 1));
    uint64_t u = 
        _mm_cvtsi128_si64(parts3) + 
        _mm_extract_epi64(parts3, 1);
    
    return u + (n & ~(n + 1)) + v;
}

As for Transpose64x8, you can find out how to implement that in Permuting bits with GF2P8AFFINEQB.