Gsyfuy

For starters, the problem of unpacking the bits, because seriously, you do not want to test each bit individually.

So just follow the following strategy for unpacking the bits into bytes of a vector: https://stackoverflow.com/a/24242696/2879325

Now that you have padded each bit to 8 bits, you can just do this for blocks of up to 255 bitmasks at a time, and accumulate them all into a single vector register. After that, you would have to expect potential overflows, so you need to transfer.

After each block of 255, unpack again to 32bit, and add into the array. (You don't have to do exactly 255, just some convenient number less than 256 to avoid overflow of byte accumulators).

At 8 instructions per bitmask (4 per each lower and higher 32-bit with AVX2) - or half that if you have AVX512 available - you should be able to achieve a throughput of about half a billion bitmasks per second and core on an recent CPU.

One way of speeding this up significantly, even without AVX, is to split the data into blocks of up to 255 elements, and accumulate the bit counts byte-wise in ordinary uint64_t variables. Since the source data has 64 bits, we need an array of 8 byte-wise accumulators. The first accumulator counts bits in positions 0, 8, 16, ... 56, second accumulator counts bits in positions 1, 9, 17, ... 57; and so on. After we are finished processing a block of data, we transfers the counts form the byte-wise accumulator into the target counts. A function to update the target counts for a block of up to 255 numbers can be coded in a straightforward fashion according to the description above, where BITS is the number of bits in the source data:

/* update the counts of 1-bits in each bit position for up to 255 numbers */

void sum_block (const uint64_t *pLong, unsigned int *target, int lo, int hi)

{

    int jj, k, kk;

    uint64_t byte_wise_sum [BITS/8] = {0};

    for (jj = lo; jj < hi; jj++) {

        uint64_t t = pLong[jj];

        for (k = 0; k < BITS/8; k++) {

            byte_wise_sum[k] += t & 0x0101010101010101;

            t >>= 1;

        }

    }

    /* accumulate byte sums into target */

    for (k = 0; k < BITS/8; k++) {

        for (kk = 0; kk < BITS; kk += 8) {

            target[kk + k] += (byte_wise_sum[k] >> kk) & 0xff;

        }

    }

}

The entire ISO-C99 program, which should be able to run on at least Windows and Linux platforms is shown below. It initializes the source data with a PRNG, performs a correctness check against the asker's reference implementation, and benchmarks both the reference code and the accelerated version. On my machine (Intel Xeon E3-1270 v2 @ 3.50 GHz), when compiled with MSVS 2010 at full optimization (/Ox), the output of the program is:

p=0000000000550040

ref took 2.020282 secs, fast took 0.027099 secs

where ref refers to the asker's original solution. The speed-up here is about a factor 74x. Different speed-ups will be observed with other (and especially newer) compilers.

#include <stdio.h>

#include <stdlib.h>

#include <stdint.h>

#include <string.h>



#if defined(_WIN32)

#if !defined(WIN32_LEAN_AND_MEAN)

#define WIN32_LEAN_AND_MEAN

#endif

#include <windows.h>

double second (void)

{

    LARGE_INTEGER t;

    static double oofreq;

    static int checkedForHighResTimer;

    static BOOL hasHighResTimer;



    if (!checkedForHighResTimer) {

        hasHighResTimer = QueryPerformanceFrequency (&t);

        oofreq = 1.0 / (double)t.QuadPart;

        checkedForHighResTimer = 1;

    }

    if (hasHighResTimer) {

        QueryPerformanceCounter (&t);

        return (double)t.QuadPart * oofreq;

    } else {

        return (double)GetTickCount() * 1.0e-3;

    }

}

#elif defined(__linux__) || defined(__APPLE__)

#include <stddef.h>

#include <sys/time.h>

double second (void)

{

    struct timeval tv;

    gettimeofday(&tv, NULL);

    return (double)tv.tv_sec + (double)tv.tv_usec * 1.0e-6;

}

#else

#error unsupported platform

#endif



/*

  From: geo <gmars...@gmail.com>

  Newsgroups: sci.math,comp.lang.c,comp.lang.fortran

  Subject: 64-bit KISS RNGs

  Date: Sat, 28 Feb 2009 04:30:48 -0800 (PST)



  This 64-bit KISS RNG has three components, each nearly

  good enough to serve alone.    The components are:

  Multiply-With-Carry (MWC), period (2^121+2^63-1)

  Xorshift (XSH), period 2^64-1

  Congruential (CNG), period 2^64

*/

static uint64_t kiss64_x = 1234567890987654321ULL;

static uint64_t kiss64_c = 123456123456123456ULL;

static uint64_t kiss64_y = 362436362436362436ULL;

static uint64_t kiss64_z = 1066149217761810ULL;

static uint64_t kiss64_t;

#define MWC64  (kiss64_t = (kiss64_x << 58) + kiss64_c, 

                kiss64_c = (kiss64_x >> 6), kiss64_x += kiss64_t, 

                kiss64_c += (kiss64_x < kiss64_t), kiss64_x)

#define XSH64  (kiss64_y ^= (kiss64_y << 13), kiss64_y ^= (kiss64_y >> 17), 

                kiss64_y ^= (kiss64_y << 43))

#define CNG64  (kiss64_z = 6906969069ULL * kiss64_z + 1234567ULL)

#define KISS64 (MWC64 + XSH64 + CNG64)



#define N          (10000000)

#define BITS       (64)

#define BLOCK_SIZE (255)



/* cupdate the count of 1-bits in each bit position for up to 255 numbers */

void sum_block (const uint64_t *pLong, unsigned int *target, int lo, int hi)

{

    int jj, k, kk;

    uint64_t byte_wise_sum [BITS/8] = {0};

    for (jj = lo; jj < hi; jj++) {

        uint64_t t = pLong[jj];

        for (k = 0; k < BITS/8; k++) {

            byte_wise_sum[k] += t & 0x0101010101010101;

            t >>= 1;

        }

    }

    /* accumulate byte sums into target */

    for (k = 0; k < BITS/8; k++) {

        for (kk = 0; kk < BITS; kk += 8) {

            target[kk + k] += (byte_wise_sum[k] >> kk) & 0xff;

        }

    }

}



int main (void) 

{

    double start_ref, stop_ref, start, stop;

    uint64_t *pLong;

    unsigned int target_ref [BITS] = {0};

    unsigned int target [BITS] = {0};

    int i, j;



    pLong = malloc (sizeof(pLong[0]) * N);

    if (!pLong) {

        printf("failed to allocaten");

        return EXIT_FAILURE;

    }

    printf("p=%pn", pLong);



    /* init data */

    for (j = 0; j < N; j++) {

        pLong[j] = KISS64;

    }



    /* count bits slowly */

    start_ref = second();

    for (j = 0; j < N; j++) {

        uint64_t m = 1;

        for (i = 0; i < BITS; i++) {

            if ((pLong[j] & m) == m) {

                target_ref[i]++;

            }

            m = (m << 1);

        }

    }

    stop_ref = second();



    /* count bits fast */

    start = second();

    for (j = 0; j < N / BLOCK_SIZE; j++) {

        sum_block (pLong, target, j * BLOCK_SIZE, (j+1) * BLOCK_SIZE);

    }

    sum_block (pLong, target, j * BLOCK_SIZE, N);

    stop = second();



    /* check whether result is correct */

    for (i = 0; i < BITS; i++) {

        if (target[i] != target_ref[i]) {

            printf ("error @ %d: res=%u ref=%un", i, target[i], target_ref[i]);

        }

    }



    /* print benchmark results */

    printf("ref took %f secs, fast took %f secsn", stop_ref - start_ref, stop - start);

    return EXIT_SUCCESS;

}

Your best bet is SIMD, using AVX1 on your Sandybridge CPU. Compilers aren't smart enough to auto-vectorize this for you, even if you write it branchlessly to give them a better chance.

See is there an inverse instruction to the movemask instruction in intel avx2? for a summary of bitmap -> vector unpack methods for different sizes. Ext3h's suggestion in another answer is the best: Unpack bits to something narrower than the final count array gives you more elements per instruction. Bytes is efficient with SIMD, and then you can do up to 255 vertical paddb without overflow, before unpacking to 32-bit counters.

It only takes 4x 16-byte __m128i vectors to hold all 64 elements, so those accumulators can stay in registers, only adding to memory when widening out to 32-bit counters in an outer loop.

The unpack doesn't have to be in-order: you can always shuffle target[] after accumulating all the results.

The inner loop could be unrolled to start with a 64 or 128-bit vector load, and unpack 4 or 8 different ways using pshufb (_mm_shuffle_epi8).

An even better strategy might be to widen gradually, starting with 2-bit accumulators and then mask/shift to widen those to 4-bit. So in the inner-most loop most of the operations are working with "dense" data.

Using SWAR techniques inside scalar or SIMD registers is easy because we need to avoid the possibility of carry out the top of an element anyway. With proper SIMD, we'd lose those counts, with SWAR we'd corrupt the next element.

uint64_t *endp = input + length - 3*2*something;  // 

while(input < endp) {

  ...

  // inner 2 loops that create 4-bit accumulators.

  for(int j=0 ; j<2 ; j++){

    uint64_t accum2_lo=0, accum2_hi=0;

    for(int i=0 ; i<3 ; i++) {  // the compiler should fully unroll this

      uint64_t x = *input++;    // load a new bitmask



      uint64_t lo = x & 0b...01010101;  // note: binary not hex constant

      uint64_t hi = (x>>1) & 0b...01010101;



      accum2_lo += lo;

      accum2_hi += hi;   // can do up to 3 iterations of this before

    }

    accum4_0 +=  accum2_lo & 0b...001100110011;   // same constant 4 times, because we shift *first*

    accum4_1 += (accum2_lo >> 2) & 0b...001100110011;

    accum4_2 +=  accum2_hi & 0b...001100110011;

    accum4_3 += (accum2_hi >> 2) & 0b...001100110011;



    // 6*2 = 12   <  15 so we can repeat this twice before widening to bytes

  }

  ...  /// accum8_0..7 the same way.



}

We don't care about order, so accum4_0 has 4-bit accumulators for every 4th bit.

We can use any SIMD element width to implement these shifts; we have to mask anyway for widths lower than 16-bit (SSE/AVX doesn't have byte-granularity shifts, only 16-bit minimum.)

But your portable scalar version can be improved, too:

If you expect a random mix of zeros and ones, you want something branchless that won't mispredict. Doing += 0 for elements that were zero avoids that, and also means that the C abstract machine definitely touches that memory regardless of the data.

Compilers aren't allowed to invent writes, so if they wanted to auto-vectorize your if() target[i]++ version, they'd have to use a masked store like x86 vmaskmovps to avoid a non-atomic read / rewrite of unmodified elements of target. So some hypothetical future compiler that can auto-vectorize the plain scalar code would have an easier time with this.

Anyway, one way to write this is target[i] += (pLong[j] & m != 0);, using bool->int conversion to get a 0 / 1 integer.

But we get better asm for x86 (and probably for most other architectures) if we just shift the data and isolate the low bit with &1. Compilers are kinda dumb and don't seem to spot this optimization. They do nicely optimize away the extra loop counter, and turn m <<= 1 into add same,same to efficiently left shift, but they still use xor-zero / test / setne to create a 0 / 1 integer.

An inner loop like this compiles slightly more efficiently (but still much much worse than we can do with SSE2 or AVX, or even scalar using @chrqlie's lookup table which will stay hot in L1d when used repeatedly like this, allowing SWAR in uint64_t):

    for (int j = 0; j < 10000000; j++) {

#if 1  // extract low bit directly

        unsigned long long tmp = pLong[j];

        for (int i=0 ; i<64 ; i++) {   // while(tmp) could mispredict, but good for sparse data

            target[i] += tmp&1;

            tmp >>= 1;

        }

#else // bool -> int shifting a mask

        unsigned long m = 1;

        for (i = 0; i < 64; i++) {

            target[i]+= (pLong[j] & m) != 0;

            m = (m << 1);

        }

#endif

Note that unsigned long is not guaranteed to be a 64-bit type, and isn't in x86-64 System V x32 (ILP32 in 64-bit mode), and Windows x64. Or in 32-bit ABIs like i386 System V.

Compiled on the Godbolt compiler explorer by gcc, clang, and ICC, it's 1 fewer uops in the loop with gcc. But all of them are just plain scalar, with clang and ICC unrolling by 2.

# clang7.0 -O3 -march=sandybridge

.LBB1_2:                            # =>This Loop Header: Depth=1

   # outer loop loads a uint64 from the src

    mov     rdx, qword ptr [r14 + 8*rbx]

    mov     rsi, -256

.LBB1_3:                            #   Parent Loop BB1_2 Depth=1

                                    # do {

    mov     edi, edx

    and     edi, 1                              # isolate the low bit

    add     dword ptr [rsi + target+256], edi   # and += into target



    mov     edi, edx

    shr     edi

    and     edi, 1                              # isolate the 2nd bit

    add     dword ptr [rsi + target+260], edi



    shr     rdx, 2                              # tmp >>= 2;



    add     rsi, 8

    jne     .LBB1_3                       # } while(offset += 8 != 0);

This is slightly better than we get from test / setnz. Without unrolling, bt / setc might have been equal, but compilers are bad at using bt to implement bool (x & (1ULL << n)), or bts to implement x |= 1ULL << n.

If many words have their highest set bit far below bit 63, looping on while(tmp) could be a win. Branch mispredicts make it not worth it if it only saves ~0 to 4 iterations most of the time, but if it often saves 32 iterations, that could really be worth it. Maybe unroll in the source so the loop only tests tmp every 2 iterations (because compilers won't do that transformation for you), but then the loop branch can be shr rdx, 2 / jnz.

On Sandybridge-family, this is 11 fused-domain uops for the front end per 2 bits of input. (add [mem], reg with a non-indexed addressing mode micro-fuses the load+ALU, and the store-address+store-data, everything else is single-uop. add/jcc macro-fuses. See Agner Fog's guide, and https://stackoverflow.com/tags/x86/info). So it should run at something like 3 cycles per 2 bits = one uint64_t per 96 cycles. (Sandybridge doesn't "unroll" internally in its loop buffer, so non-multiple-of-4 uop counts basically round up, unlike on Haswell and later).

vs. gcc's not-unrolled version being 7 uops per 1 bit = 2 cycles per bit. If you compiled with gcc -O3 -march=native -fprofile-generate / test-run / gcc -O3 -march=native -fprofile-use, profile-guided optimization would enable loop unrolling.

This is probably slower than a branchy version on perfectly predictable data like you get from memset with any repeating byte pattern. I'd suggest filling your array with randomly-generated data from a fast PRNG like an SSE2 xorshift+, or if you're just timing the count loop then use anything you want, like rand().