Question

我正在尝试将以下代码从SSE转换为Apple的64位iOS设备的NEON：

void Matrix::TransformPoint( const float vec[ 4 ], const Matrix& matTrans, float out[ 4 ] )
{
    alignas( 16 ) float v4[ 4 ] = { vec[ 0 ], vec[ 1 ], vec[ 2 ], vec[ 3 ] };

    __m128 vec4 = _mm_load_ps( v4 );
    __m128 row1 = _mm_load_ps( &matTrans.m[  0 ] );
    __m128 row2 = _mm_load_ps( &matTrans.m[  4 ] );
    __m128 row3 = _mm_load_ps( &matTrans.m[  8 ] );
    __m128 row4 = _mm_load_ps( &matTrans.m[ 12 ] );

    __m128 r1 = _mm_mul_ps( row1, vec4 );
    __m128 r2 = _mm_mul_ps( row2, vec4 );
    __m128 r3 = _mm_mul_ps( row3, vec4 );
    __m128 r4 = _mm_mul_ps( row4, vec4 );

    __m128 sum_01 = _mm_hadd_ps( r1, r2 );
    __m128 sum_23 = _mm_hadd_ps( r3, r4 );
    __m128 result = _mm_hadd_ps( sum_01, sum_23 );
    _mm_store_ps( out, result );
}

这是我到目前为止所拥有的：

alignas( 16 ) float v4[ 4 ] = { vec[ 0 ], vec[ 1 ], vec[ 2 ], vec[ 3 ] };

float32x4_t vec4 = vld1q_f32( v4 );
float32x4_t row1 = vld1q_f32( &mat.m[  0 ] );
float32x4_t row2 = vld1q_f32( &mat.m[  4 ] );
float32x4_t row3 = vld1q_f32( &mat.m[  8 ] );
float32x4_t row4 = vld1q_f32( &mat.m[ 12 ] );

float32x4_t r1 = vmulq_f32( row1, vec4 );
float32x4_t r2 = vmulq_f32( row2, vec4 );
float32x4_t r3 = vmulq_f32( row3, vec4 );
float32x4_t r4 = vmulq_f32( row4, vec4 );

float32x4_t sum_01 = ??? <-- How to write this?
float32x4_t sum_23 = ??? <-- How to write this?
float32x4_t result = ??? <-- How to write this?
vst1q_f32( out, result );

如何替换_mm_hadd_ps？

Answer 1

我同意其他要重写此功能以避免水平和的海报。有关示例，请参见DirectXMath：

inline XMVECTOR XM_CALLCONV XMVector4Transform
(
    FXMVECTOR V,
    FXMMATRIX M
)
{
#if defined(_XM_NO_INTRINSICS_)
    float fX = (M.m[0][0]*V.vector4_f32[0])+(M.m[1][0]*V.vector4_f32[1])+(M.m[2][0]*V.vector4_f32[2])+(M.m[3][0]*V.vector4_f32[3]);
    float fY = (M.m[0][1]*V.vector4_f32[0])+(M.m[1][1]*V.vector4_f32[1])+(M.m[2][1]*V.vector4_f32[2])+(M.m[3][1]*V.vector4_f32[3]);
    float fZ = (M.m[0][2]*V.vector4_f32[0])+(M.m[1][2]*V.vector4_f32[1])+(M.m[2][2]*V.vector4_f32[2])+(M.m[3][2]*V.vector4_f32[3]);
    float fW = (M.m[0][3]*V.vector4_f32[0])+(M.m[1][3]*V.vector4_f32[1])+(M.m[2][3]*V.vector4_f32[2])+(M.m[3][3]*V.vector4_f32[3]);
    XMVECTORF32 vResult = { { { fX, fY, fZ, fW } } };
    return vResult.v;
#elif defined(_XM_ARM_NEON_INTRINSICS_)
    float32x2_t VL = vget_low_f32( V );
    XMVECTOR vResult = vmulq_lane_f32( M.r[0], VL, 0 ); // X
    vResult = vmlaq_lane_f32( vResult, M.r[1], VL, 1 ); // Y
    float32x2_t VH = vget_high_f32( V );
    vResult = vmlaq_lane_f32( vResult, M.r[2], VH, 0  ); // Z
    return vmlaq_lane_f32( vResult, M.r[3], VH, 1 ); // W
#elif defined(_XM_SSE_INTRINSICS_)
    // Splat x,y,z and w
    XMVECTOR vTempX = XM_PERMUTE_PS(V,_MM_SHUFFLE(0,0,0,0));
    XMVECTOR vTempY = XM_PERMUTE_PS(V,_MM_SHUFFLE(1,1,1,1));
    XMVECTOR vTempZ = XM_PERMUTE_PS(V,_MM_SHUFFLE(2,2,2,2));
    XMVECTOR vTempW = XM_PERMUTE_PS(V,_MM_SHUFFLE(3,3,3,3));

    // Mul by the matrix
    vTempX = _mm_mul_ps(vTempX,M.r[0]);
    vTempY = _mm_mul_ps(vTempY,M.r[1]);
    vTempZ = _mm_mul_ps(vTempZ,M.r[2]);
    vTempW = _mm_mul_ps(vTempW,M.r[3]);

    // Add them all together
    vTempX = _mm_add_ps(vTempX,vTempY);
    vTempZ = _mm_add_ps(vTempZ,vTempW);
    vTempX = _mm_add_ps(vTempX,vTempZ);
    return vTempX;
#endif
}

那是为了回答有关水平向量加法的明确问题，我使用了两个成对加法。对于ARMv8 / ARM64，有一个vpaddq_f32，它仅用两条指令即可求和4个值：

inline XMVECTOR XM_CALLCONV XMVectorSum
(
    FXMVECTOR V
)
{
#if defined(_XM_NO_INTRINSICS_)
    XMVECTORF32 Result;
    Result.f[0] =
    Result.f[1] =
    Result.f[2] =
    Result.f[3] = V.vector4_f32[0] + V.vector4_f32[1] + V.vector4_f32[2] + V.vector4_f32[3];
    return Result.v;
#elif defined(_XM_ARM_NEON_INTRINSICS_)
#if defined(_M_ARM64) || defined(_M_HYBRID_X86_ARM64) || __aarch64__
    XMVECTOR vTemp = vpaddq_f32(V, V);
    return vpaddq_f32(vTemp,vTemp);
#else
    float32x2_t v1 = vget_low_f32(V);
    float32x2_t v2 = vget_high_f32(V);
    v1 = vadd_f32(v1, v2);
    v1 = vpadd_f32(v1, v1);
    return vcombine_f32(v1, v1);
#endif
#elif defined(_XM_SSE3_INTRINSICS_)
    XMVECTOR vTemp = _mm_hadd_ps(V, V);
    return _mm_hadd_ps(vTemp,vTemp);
#elif defined(_XM_SSE_INTRINSICS_)
    XMVECTOR vTemp = XM_PERMUTE_PS(V, _MM_SHUFFLE(2, 3, 0, 1));
    XMVECTOR vTemp2 = _mm_add_ps(V, vTemp);
    vTemp = XM_PERMUTE_PS(vTemp2, _MM_SHUFFLE(1, 0, 3, 2));
    return _mm_add_ps(vTemp, vTemp2);
#endif
}

Answer 2

为回答实际问题，neon具有成对添加，与sse的水平添加相同。寻找vpadd_f32。

Answer 3

这是典型的矩阵矢量乘法，最好进行矩阵转置，然后再进行一系列矢量标量乘法累加运算，从而避免了费时的水平加法运算：

float32x4x4_t mat;
float32x4_t vec4, result;

mat = vld4q_f32(pMat);
vec4 = vld1q_f32(pVec);

result = vmulq_lane_f32(mat.val[0], vget_low_f32(vec4), 0);
result = vmlaq_lane_f32(result, mat.val[1], vget_low_f32(vec4), 1);
result = vmlaq_lane_f32(result, mat.val[2], vget_high_f32(vec4), 0);
result = vmlaq_lane_f32(result, mat.val[3], vget_high_f32(vec4), 1);

vst1q_f32(pDst, result);

NEON中的_mm_hadd_ps等于多少？

3 个答案: