为什么对Aligned STD ::数组的初始自动矢量载荷是标量?(g /clang ）

我有问题理解是什么防止编译器从 std :: array＆lt; uint64_t，...> 。。

我知道GCC可以使用-FOPT-INFO-VEC-*产生调试信息。我从详细日志中找不到任何东西，这些都表明了为什么两个编译器都会做出相同的次级决定使用初始标量负载。

另一方面，我不知道如何使Clang提供有关矢量化问题的详细信息。-RPASS-分析=循环 - 矢量化仅报告INIT中的循环不值得交错。当然，我的内在版本证明了循环可以进行矢量化，但所需的转换可能太复杂了，除了编译器外，还无法进行循环。

我当然可以使用固有的插入来实现热路，但这需要为每个CPU辩论复制相同的逻辑。我更喜欢编写编译器可以完美矢量化的Stanard C 代码。使用target_clones属性或宏和目标属性多次多次编译相同的代码。

如何使编译器告诉为什么负载无法矢量化？

我怀疑GCC可能已经打印了该信息，我只是不知道我在寻找什么。

为什么自动矢量化在初始加载时失败？

    /**
     * This is a test case removing abstraction layers from my actual code. My
     * real code includes one extra problem that access to pack loses alignment
     * information wasn't only issue. Compilers still generate
     * suboptimal machine code with alignment information present. I fail to
     * understand why loads are treated differently compared to stores to
     * same address when auto-vectorization is used.
     *
     * I tested gcc 6.2 and clang 3.9
     * g++ O3 -g -march=native vectest.cc -o vectest -fvect-cost-model=unlimited
     * clang++ -O3 -g -march=native vectest.cc -o vectest
     */

    #include <array>
    #include <cstdint>
    alignas(32) std::array<uint64_t, 52> pack;
    alignas(32) uint64_t board[4];
    __attribute__((noinline))
    static void init(uint64_t initial)
    {
        /* Clang seem to prefer large constant table and unrolled copy
         * which should perform worse outside micro benchmark. L1 misses
         * and memory bandwidth are bigger bottleneck than alu instruction
         * execution. But of course this code won't be compiled to hot path so
         * I don't care how it is compiled as long as it works correctly.
         *
         * But most interesting detail from clang is vectorized stores are
         * generated correctly like:
    4005db:       vpsllvq %ymm2,%ymm1,%ymm2
    4005e0:       vmovdqa %ymm2,0x200a78(%rip)        # 601060 <pack>
    4005e8:       vpaddq 0x390(%rip),%ymm0,%ymm2        # 400980 <_IO_stdin_used+0x60>
    4005f0:       vpsllvq %ymm2,%ymm1,%ymm2
    4005f5:       vmovdqa %ymm2,0x200a83(%rip)        # 601080 <pack+0x20>
    4005fd:       vpaddq 0x39b(%rip),%ymm0,%ymm2        # 4009a0 <_IO_stdin_used+0x80>
         *
         * gcc prefers scalar loop.
         */
        for (unsigned i = 0; i < pack.size(); i++) {
            pack[i] = 1UL << (i + initial);
        }
    }
    #include "immintrin.h"
    __attribute__((noinline))
    static void expected_init(uint64_t initial)
    {
        /** Just an intrinsic implementation of init that would be IMO ideal
         * optimization.
         */
    #if __AVX2__
        unsigned i;
        union {
            uint64_t *mem;
            __m256i *avx;
        } conv;
        conv.mem = &pack[0];
        __m256i t = _mm256_set_epi64x(
                1UL << 3,
                1UL << 2,
                1UL << 1,
                1UL << 0
                );
        /* initial is just extra random number to prevent constant array
         * initialization
         */
        t = _mm256_slli_epi64(t, initial);
        for(i = 0; i < pack.size()/4; i++) {
            _mm256_store_si256(&conv.avx[i], t);
            t = _mm256_slli_epi64(t, 4);
        }
    #endif
    }
    __attribute__((noinline))
    static void iter_or()
    {
        /** initial load (clang):
    4006f0:       vmovaps 0x200988(%rip),%xmm0        # 601080 <pack+0x20>
    4006f8:       vorps  0x200960(%rip),%xmm0,%xmm0        # 601060 <pack>
    400700:       vmovaps 0x200988(%rip),%xmm1        # 601090 <pack+0x30>
    400708:       vorps  0x200960(%rip),%xmm1,%xmm1        # 601070 <pack+0x10>
    400710:       vinsertf128 $0x1,%xmm1,%ymm0,%ymm0
        * expected:
    400810:       vmovaps 0x200868(%rip),%ymm0        # 601080 <pack+0x20>
    400818:       vorps  0x200840(%rip),%ymm0,%ymm0        # 601060 <pack>
    400820:       vorps  0x200878(%rip),%ymm0,%ymm0        # 6010a0 <pack+0x40>
        */
        auto iter = pack.begin();
        uint64_t n(*iter++),
             e(*iter++),
             s(*iter++),
             w(*iter++);
        for (;iter != pack.end();) {
            n |= *iter++;
            e |= *iter++;
            s |= *iter++;
            w |= *iter++;
        }
        /** Store is correctly vectorized to single instruction */
        board[0] = n;
        board[1] = e;
        board[2] = s;
        board[3] = w;
    }
    __attribute__((noinline))
    static void index_or()
    {
        /** Clang compiles this to same as iterator variant. gcc goes
         * completely insane. I don't even want to try to guess what all the
         * permutation stuff is trying to archive.
         */
        unsigned i;
        uint64_t n(pack[0]),
             e(pack[1]),
             s(pack[2]),
             w(pack[3]);
        for (i = 4 ; i < pack.size(); i+=4) {
            n |= pack[i+0];
            e |= pack[i+1];
            s |= pack[i+2];
            w |= pack[i+3];
        }
        board[0] = n;
        board[1] = e;
        board[2] = s;
        board[3] = w;
    }
    #include "immintrin.h"
    __attribute__((noinline))
    static void expected_result()
    {
        /** Intrinsics implementation what I would expect auto-vectorization
         * transform my c++ code. I simple can't understand why both compilers
         * fails to archive results I expect.
         */
    #if __AVX2__
        union {
            uint64_t *mem;
            __m256i *avx;
        } conv;
        conv.mem = &pack[0];
        unsigned i;
        __m256i res = _mm256_load_si256(&conv.avx[0]);
        for (i = 1; i < pack.size()/4; i++) {
            __m256i temp = _mm256_load_si256(&conv.avx[i]);
            res = _mm256_or_si256(res, temp);
        }
        conv.mem = board;
        _mm256_store_si256(conv.avx, res);
    #endif
    }
    int main(int c, char **v)
    {
        (void)v;
        expected_init(c - 1);
        init(c - 1);
        iter_or();
        index_or();
        expected_result();
    }