NEON内在vs C ++

问题描述

我试图在 ARM 中击败 Gcc 编译器,我花了很多时间,但似乎我是否编译了用编写的代码- o3 标志比在我的代码中使用 NEON内在更快。 实际上,此代码是具有c ++代码 gaussianBlur 过滤器:

inline void GaussianBlur_5x5_row(const float __restrict_arr *in,float __restrict_arr *out,const unsigned int cols)
{
    //Left columns
    out[0] = (in[0]+in[2])*0.054488685f + (in[0]+in[1])*0.24420135f + in[0]*0.40261996f;
    out[1] = (in[0]+in[3])*0.054488685f + (in[0]+in[2])*0.24420135f + in[1]*0.40261996f;

    //Middle columns (Tiled)
    for (unsigned int j=2; j<cols-2; j+=2)
    {
        out[j  ] = (in[j-2]+in[j+2])*0.054488685f + (in[j-1]+in[j+1])*0.24420135f + in[j  ]*0.40261996f;
        out[j+1] = (in[j-1]+in[j+3])*0.054488685f + (in[j  ]+in[j+2])*0.24420135f + in[j+1]*0.40261996f;


    }
    //Right columns
    out[cols-2] = (in[cols-4]+in[cols-1])*0.054488685f + (in[cols-3]+in[cols-1])*0.24420135f + in[cols-2]*0.40261996f;
    out[cols-1] = (in[cols-3]+in[cols-1])*0.054488685f + (in[cols-2]+in[cols-1])*0.24420135f + in[cols-1]*0.40261996f;
}



void GaussianBlur_5x5(const Matrixf &input,Matrixf &output)
{
    //sigma is 1.0f
    //kernels prevIoUsly calculated from MATLAB code
    //border type is replicate

    const unsigned int rows = input.rows;
    const unsigned int cols = input.cols;

    output.resize(rows,cols);

    float buffer[cols] __attribute__((aligned(16)));

    //Top rows
    #pragma omp parallel for private(buffer)
    for (unsigned int i=0; i<2; i++)
    {
        for (unsigned int j=0; j<cols; j++)
            buffer[j] = (input.val[0][j]+input.val[i+2][j])*0.054488685f + (input.val[0][j]+input.val[i+1][j])*0.24420135f + input.val[i][j]*0.40261996f;

        GaussianBlur_5x5_row(buffer,output.val[i],cols);
    }

    //Middle rows
    #pragma omp parallel for private(buffer) schedule(runtime)
    for (unsigned int i=2; i<rows-2; i++)
    {
        for (unsigned int j=0; j<cols; j++)
            buffer[j] = (input.val[i-2][j]+input.val[i+2][j])*0.054488685f + (input.val[i-1][j]+input.val[i+1][j])*0.24420135f + input.val[i][j]*0.40261996f;

        GaussianBlur_5x5_row(buffer,cols);
    }

    //Bottom rows
    #pragma omp parallel for private(buffer)
    for (unsigned int i=rows-2; i<rows; i++)
    {
        for (unsigned int j=0; j<cols; j++)
            buffer[j] = (input.val[i-2][j]+input.val[rows-1][j])*0.054488685f + (input.val[i-1][j]+input.val[rows-1][j])*0.24420135f + input.val[i][j]*0.40261996f;

        GaussianBlur_5x5_row(buffer,cols);
    }
}

,在以下代码中,我正在使用NEON内部函数

#include <arm_neon.h>

inline void GaussianBlur5x5_row_rc( const float __restrict_arr *in,const unsigned int cols)
{
    //Left columns
    out[0] = (in[0]+in[2])*0.054488685f + (in[0]+in[1])*0.24420135f + in[0]*0.40261996f;
    out[1] = (in[0]+in[3])*0.054488685f + (in[0]+in[2])*0.24420135f + in[1]*0.40261996f;


     const float32x4_t coef_1 = { 0.054488685f,0.122100675f,0.20130998f,0.122100675f };
     const float32x4_t coef_2 = { 0.122100675f,0.054488685f };



    //Middle columns
    for (unsigned int j=2; j<cols-2; j+=2)
    {

        out[j  ] = (in[j-2]+in[j+2])*0.054488685f + (in[j-1]+in[j+1])*0.24420135f + in[j  ]*0.40261996f;
        out[j+1] = (in[j-1]+in[j+3])*0.054488685f + (in[j  ]+in[j+2])*0.24420135f + in[j+1]*0.40261996f

    }
    //Right columns
    out[cols-2] = (in[cols-4]+in[cols-1])*0.054488685f + (in[cols-3]+in[cols-1])*0.24420135f + in[cols-2]*0.40261996f;
    out[cols-1] = (in[cols-3]+in[cols-1])*0.054488685f + (in[cols-2]+in[cols-1])*0.24420135f + in[cols-1]*0.40261996f;
}


void GaussianBlur5x5_NEON_rc( const Matrixf &input,cols);

    float buffer[cols] __attribute__((aligned(16)));

    //Top rows
    #pragma omp parallel for private(buffer)

    for (unsigned int i=0; i<2; i++)
    {
        for (unsigned int j=0; j<cols; j++)
            buffer[j] = (input.val[0][j]+input.val[i+2][j])*0.054488685f + (input.val[0][j]+input.val[i+1][j])*0.24420135f + input.val[i][j]*0.40261996f;

        GaussianBlur5x5_row_rc(buffer,cols);
    }



    const float32x4_t a0 = {0.054488685f,0.054488685f,0.054488685f };
    const float32x4_t a1 = {0.24420135f,0.24420135f,0.24420135f  };
    const float32x4_t a2 = {0.40261996f,0.40261996f,0.40261996f  };


    //Middle rows
    //#pragma omp parallel for private(buffer) schedule(runtime)
    for (unsigned int i=2; i<rows-2; i++)
    {
        for (unsigned int j=0; j<cols; j+=4)
        {
            float32x4_t row_0 = vld1q_f32( &input.val[i-2][j] );
            float32x4_t row_1 = vld1q_f32( &input.val[i-1][j] );
            float32x4_t row_2 = vld1q_f32( &input.val[i  ][j] );
            float32x4_t row_3 = vld1q_f32( &input.val[i+1][j] );
            float32x4_t row_4 = vld1q_f32( &input.val[i+2][j] );


            float32x4_t h_0 = vaddq_f32( row_4,row_0 );                   ///// vmlaq
            float32x4_t h_1 = vaddq_f32( row_3,row_1 );
            float32x4_t h_2 = vmulq_f32( h_0,a0    );

                        h_0 = vmlaq_f32( h_2,h_1,a1 );
                        h_1 = vmlaq_f32( h_0,row_2,a2 );

            vst1q_f32( &buffer[j],h_1 );

        }
        GaussianBlur5x5_row_rc(buffer,cols);
    }

    //Bottom rows
    #pragma omp parallel for private(buffer)
    for (unsigned int i=rows-2; i<rows; i++)
    {
        for (unsigned int j=0; j<cols; j++)
            buffer[j] = (input.val[i-2][j]+input.val[rows-1][j])*0.054488685f + (input.val[i-1][j]+input.val[rows-1][j])*0.24420135f + input.val[i][j]*0.40261996f;

        GaussianBlur5x5_row_rc(buffer,cols);
    }
}



然后我决定使用内联汇编,但是代码的结果比其他代码慢得多

inline void GaussianBlur_5x5_row_asm(const float __restrict_arr *in,const unsigned int cols)
{
    //Left columns
    out[0] = (in[0]+in[2])*0.054488685f + (in[0]+in[1])*0.24420135f + in[0]*0.40261996f;
    out[1] = (in[0]+in[3])*0.054488685f + (in[0]+in[2])*0.24420135f + in[1]*0.40261996f;


    const float c1[4] = { 0.054488685f,0.122100675f };
    const float c2[4] = { 0.122100675f,0.054488685f };

    //Middle columns (Tiled)
    for (unsigned int j=2; j<cols-2; j+=2)
    {
    asm volatile(
                "mov r0,%[co1];"
                "mov r1,%[co2];"
                "mov r2,%[in_j_n2];"
                "mov r3,%[in_j_n1];"
                "mov r4,%[in_j];"

                "vld1.32 {d0,d1},[r0];"                    // q0 = c1
                "vld1.32 {d2,d3},[r1];"                    // q1 = c2
                "vld1.32 {d4,d5},[r2];"                    // q2 = in[j-2]
                "vld1.32 {d6,d7},[r3];"                    // q3 = in[j-1]
                "vld1.32 {d8,d9},[r4];"                    // q4 = in[j]

                "vmul.f32 q5,q2,q0;"
                "vmul.f32 q6,q3,q1;"
                "vadd.f32 q7,q6,q5;"
                "vadd.f32 d16,d15,d14;"
                "vpadd.f32 d17,d16,d16;"               // out[j]

                "vmul.f32 q5,q4,q5;"
                "vadd.f32 d18,d14;"
                "vpadd.f32 d19,d18,d18;"               // out[j+1]

                "mov r5,%[out_j];"
                "mov r6,%[out_j_p1];"
                "vst1.32 d17[0],[r5];"
                "vst1.32 d19[0],[r6];"

                :
                :[co1] "r" (c1),[co2] "r" (c2),[in_j_n2] "r" (&in[j-2]),[in_j_n1] "r" (&in[j-1]),[in_j] "r" (&in[j]),[out_j] "r" (&out[j]),[out_j_p1] "r" (&out[j+1])
                :"r0","r1","r2","r3"//,"q0","q1","q2","q3","q4","q5","q6","q7","d16","d17","d18","d19","r5","r6"
                );

    }
    //Right columns
    out[cols-2] = (in[cols-4]+in[cols-1])*0.054488685f + (in[cols-3]+in[cols-1])*0.24420135f + in[cols-2]*0.40261996f;
    out[cols-1] = (in[cols-3]+in[cols-1])*0.054488685f + (in[cols-2]+in[cols-1])*0.24420135f + in[cols-1]*0.40261996f;
}



void GaussianBlur_5x5_asm(const Matrixf &input,cols);

    float buffer[cols] __attribute__((aligned(16)));

    //Top rows
    #pragma omp parallel for private(buffer)
    for (unsigned int i=0; i<2; i++)
    {
        for (unsigned int j=0; j<cols; j++)
        {
            buffer[j] = (input.val[0][j]+input.val[i+2][j])*0.054488685f + (input.val[0][j]+input.val[i+1][j])*0.24420135f + input.val[i][j]*0.40261996f;
        }
        GaussianBlur_5x5_row_asm(buffer,cols);
    }

    const float a0[4] = {0.054488685f,0.054488685f};
    const float a1[4] = {0.24420135f,0.24420135f };
    const float a2[4] = {0.40261996f,0.40261996f };



    //Middle rows
    //#pragma omp parallel for private(buffer) schedule(runtime)
    for (unsigned int i=2; i<rows-2; i++)
    {
    for (unsigned int j=0; j<cols; j+=4)
    {
        //  buffer[j] = (input.val[i-2][j]+input.val[i+2][j])*0.054488685f + (input.val[i-1][j]+input.val[i+1][j])*0.24420135f + input.val[i][j]*0.40261996f;

        asm volatile(
                    "mov r0,%[c0];"
                    "mov r1,%[c1];"
                    "mov r2,%[c2];"

                    "mov r3,%[in_0];"
                    "mov r4,%[in_1];"
                    "mov r5,%[in_2];"
                    "mov r6,%[in_3];"
                    "mov r7,%[in_4];"

                    "vld1.32 {d0,[r0];"    //  q0 = a0
                    "vld1.32 {d2,[r1];"    //  q1 = a1
                    "vld1.32 {d4,[r2];"    //  q2 = a2
                    "vld1.32 {d6,[r3];"    //  q3 = input.val[i-2]
                    "vld1.32 {d8,[r4];"    //  q4 = input.val[i-1]
                    "vld1.32 {d10,d11},[r5];"    //  q5 = input.val[i  ]
                    "vld1.32 {d12,d13},[r6];"    //  q6 = input.val[i+1]
                    "vld1.32 {d14,d15},[r7];"    //  q7 = input.val[i+2]

                    "vmul.f32 q8,q0;"
                    "vmul.f32 q9,q1;"
                    "vmul.f32 q10,q5,q2;"
                    "vmul.f32 q11,q1;"
                    "vmul.f32 q12,q7,q0;"

                    "vadd.f32 q0,q9,q8 ;"
                    "vadd.f32 q1,q10,q11;"
                    "vadd.f32 q2,q1,q0 ;"
                    "vadd.f32 q3,q12,q2 ;"         // final

                    "mov r8,%[out_buffer];"
                    "vst1.32 {d6,[r8];"
                    :
                    :[c0]"r"(a0),[c1]"r"(a1),[c2]"r"(a2),[in_0]"r"(&input.val[i-2][j]),[in_1]"r"(&input.val[i-1][j]),[in_2]"r"(&input.val[i][j]),[in_3]"r"(&input.val[i+1][j]),[in_4]"r"(&input.val[i+2][j]),[out_buffer]"r"(&buffer[j])
                    :"r0","r3"
                    );
    }
    GaussianBlur_5x5_row_asm(buffer,cols);
    }

    //Bottom rows
    //#pragma omp parallel for private(buffer)
    for (unsigned int i=rows-2; i<rows; i++)
    {
        for (unsigned int j=0; j<cols; j++)
            buffer[j] = (input.val[i-2][j]+input.val[rows-1][j])*0.054488685f + (input.val[i-1][j]+input.val[rows-1][j])*0.24420135f + input.val[i][j]*0.40261996f;

        GaussianBlur_5x5_row_asm(buffer,cols);
    }
}

如果使用-o3标志,是否有可能击败GCC编译器?

解决方法

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