opencl gauss filter优化(一)

时间：2022-03-22 11:44:54

Platform: LG G3, Adreno 330 ,img size 3264x2448

C code

neon

GPU

300

60

29

单位:ms

1.
目前按如下行列分解的方式最快29ms,Horizontal
kernel
globalWorksize[1] = {height+256-height%256};Vertical kernel
globalWorksize2[1] = {width+256-width%256};

localWorksize2[]
= {64}; localWorksize2 手动设为64时最快。

Porfile的结果为:Horizontal
kernel 的wait
time 有11ms,实际rum
time 18ms.

这个wait
time是什么呢？注释掉Horizontal
kernel中的
vstore16(convert_uchar16(sum>>(ushort)8),0,pOutLine+j)
; 则wait
time只有0.x
ms.并且
localWorksize
越小wait
time越长,为1时达到200ms,16时20ms.
难道是写内存等待时间，没有足够的ALU指令隐藏访存延时？写内存后进入下一个for循环，马上又读内存，所以没有ALU指令隐藏这个延时。然而Horizontal
kernel的profile结果实际run
time只有0.x
ms,所有时间基本都是在wait.(更正：注释掉vstore16后,sum的计算被优化掉了，0.x
ms是读内存的时间)

__kernel void ImageGaussianFilterHorizontal(__global const uchar* restrict source, // Source image

                            __global uchar* restrict  dest,  // Intermediate dest image

                                             const int imgWidth ,                // Image width

                                             const int imgHeight)

{

    const int y = get_global_id();

    if(y>=(imgHeight))

        return;

    const uchar m_nRightShiftNum = ;

    const uchar Rounding = ( << (m_nRightShiftNum - ));

    const uchar  m_nFilter[] = {,,,,,,,,,,};

    const int s = ;

    const int nStart = ;

    const int nWidth = imgWidth;

    __global const uchar* pInLine = source + y*nWidth;

    __global uchar* pOutLine = dest + y*nWidth;

    int j;

    for(j = ; j < nStart; j ++)

    {

        ushort sum = ;

        for (int m = ; m<s / ; m++)

        {

            int k1 = (j + m - nStart);

            k1 = k1< ? -k1 : k1;

            int k2 = (j + nStart - m );

            sum += (pInLine[k1] + pInLine[k2])*m_nFilter[m];

        }

        sum += pInLine[j] * m_nFilter[s / ];

        sum = (sum + Rounding) >> ;

        pOutLine[j] = (uchar)clamp(sum,(ushort),(ushort));

    }

    for ( ; (j+)<= (nWidth - nStart); j+=)

    {

#define GAUSSIAN_LINE_NEON(m) \

sum += ( convert_ushort16(vload16(,pInLine+j-nStart+m))* m_nFilter[m] );

        ushort16 sum =  (convert_ushort16(vload16(,pInLine+j-nStart)) * m_nFilter[]);

        GAUSSIAN_LINE_NEON();

        GAUSSIAN_LINE_NEON();

        GAUSSIAN_LINE_NEON();

        GAUSSIAN_LINE_NEON();

        GAUSSIAN_LINE_NEON();

        GAUSSIAN_LINE_NEON();

        GAUSSIAN_LINE_NEON();

        GAUSSIAN_LINE_NEON();

        GAUSSIAN_LINE_NEON();

        GAUSSIAN_LINE_NEON();

        sum += (ushort)Rounding;

        vstore16(convert_uchar16(sum>>(ushort)),,pOutLine+j) ;

    }

    for( ; j < nWidth; j ++)

    {

        ushort sum = ;

        for (int m = ; m<s / ; m++)

        {

            int k1 = (j + m - nStart);

            int k2 = (j + nStart - m );

            k2 = k2 >= nWidth ?  * nWidth -  - k2 : k2;

            sum += (pInLine[k1] + pInLine[k2])*m_nFilter[m];

        }

        sum += pInLine[j] * m_nFilter[s / ];

        sum = (sum + Rounding) >> m_nRightShiftNum;

        pOutLine[j] =  (uchar)clamp(sum,(ushort),(ushort));

    }

}

__kernel void ImageGaussianFilterVertical( __global uchar* restrict source,   // Intermediate image processed by ImageGaussianFilterHorizontal()

                        __global uchar* restrict dest,  // Final destination image

                        const int imgWidth,

                                         const int imgHeight

                                    )

{

    const int x = get_global_id();

    if(x>=(imgWidth))

        return;

    const int x_offset = x;

    const int s = ;

    const int nStart = s / ;

    const int m_nRightShiftNum = ;

    const int Rounding = ( << (m_nRightShiftNum - ));

    const uchar  m_nFilter[] = {,,,,,,,,,,};

    int y;

//    mem_fence(CLK_LOCAL_MEM_FENCE);

    ushort lines[];

    lines[nStart] = (ushort)( source[x_offset]  );

    for(y=;y<=nStart;y++)

    {

        lines[nStart+y] = (ushort)( source[y*imgWidth+x_offset]  );

        lines[nStart-y] = lines[nStart+y];

    }

    for(y=;y<(imgHeight-nStart-);)

    {

        ushort sum = lines[nStart] * m_nFilter[nStart];

#define    GaussianTwoLines(m) \

    sum += ( (lines[m] + lines[s--m])*m_nFilter[m] );

        GaussianTwoLines()

        GaussianTwoLines()

        GaussianTwoLines()

        GaussianTwoLines()

        GaussianTwoLines()

        sum += (ushort)Rounding;

        dest[y*imgWidth+x_offset]  = (uchar)(sum>>(ushort));

        y++;

        for(int i = ; i<s-; i++) lines[i] = lines[i+];

        lines[s-] =  (ushort)( source[(y+nStart)*imgWidth+x_offset]  );

    }

    for(y=imgHeight-nStart-;y<(imgHeight-);)

    {

        ushort sum = lines[nStart] * m_nFilter[nStart];

        GaussianTwoLines()

        GaussianTwoLines()

        GaussianTwoLines()

        GaussianTwoLines()

        GaussianTwoLines()

        sum += (ushort)Rounding;

        dest[y*imgWidth+x_offset]  = (uchar)(sum>>(ushort));

        y++;

        for(int i = ; i<s-; i++) {

            lines[i] = lines[i+];

        }

        lines[s-] = lines[(imgHeight-y)*-] ; //

    }

    //last y=imgHeight-1

    ushort sum = lines[nStart] * m_nFilter[nStart];

    GaussianTwoLines()

    GaussianTwoLines()

    GaussianTwoLines()

    GaussianTwoLines()

    GaussianTwoLines()

    sum += (ushort)Rounding;

    dest[y*imgWidth+x_offset]  = (uchar)(sum>>(ushort));

}

kernel

2.Horizontal kernel改进，预先load 2x16个所需的pixel,计算时从中提取,这样每次循环只需读一次内存。需要26ms,wait time 8ms.

    ushort16 line0 =  convert_ushort16(vload16(,pInLine+j-nStart));

    for ( ; (j+)<= (nWidth - nStart); j+=)

    {

        ushort16 line1 =  convert_ushort16(vload16(,pInLine+j-nStart+));

        ushort16 temp0;

        ushort16 temp1;

        temp0 = line0;

        temp1.s0123 = line0.sabcd;

        temp1.s45 = line0.sef;

        temp1.s67 = line1.s01;

        temp1.s89abcdef = line1.s23456789;

        ushort16 sum =  ( temp0 + temp1 ) * m_nFilter[];

        temp0.s0123456789abcdef = temp0.s123456789abcdeff;

        temp0.sf = line1.s0;

        temp1.s0123456789abcdef = temp1.s00123456789abcde;

        temp1.s0 = line0.s9;

        sum += ( temp0 +  temp1 ) * m_nFilter[];

        temp0.s0123456789abcdef = temp0.s123456789abcdeff;

        temp0.sf = line1.s1;

        temp1.s0123456789abcdef = temp1.s00123456789abcde;

        temp1.s0 = line0.s8;

        sum += ( temp0 +  temp1 ) * m_nFilter[];

        temp0.s0123456789abcdef = temp0.s123456789abcdeff;

        temp0.sf = line1.s2;

        temp1.s0123456789abcdef = temp1.s00123456789abcde;

        temp1.s0 = line0.s7;

        sum += ( temp0 +  temp1 ) * m_nFilter[];

        temp0.s0123456789abcdef = temp0.s123456789abcdeff;

        temp0.sf = line1.s3;

        temp1.s0123456789abcdef = temp1.s00123456789abcde;

        temp1.s0 = line0.s6;

        sum += ( temp0 +  temp1 ) * m_nFilter[];

        temp0.s0123456789abcdef = temp0.s123456789abcdeff;

        temp0.sf = line1.s4;

        sum += ( temp0 ) * m_nFilter[];

        sum += (ushort)Rounding;

        line0 = line1;

        vstore16(convert_uchar16(sum>>(ushort)),,pOutLine+j) ;

    }

3.不计算，只读写内存测试。那么wait time 3.2 ms,run time 18.2 ms.说明Horizontal kernel 耗时的极限也需3.2ms. 但是只是注释掉vstore16，还保留了读和计算，反而wait time还只有0.x ms，这又是为何？是读几乎没有wait,3.2ms都是写的wait time? (更正：注释掉vstore16后,sum的计算被优化掉了，0.x ms是读内存的时间)

a.再次测试，只有读wait time 0.xms ,只有写wait time 3.2ms.写比读的周期长.

for ( ; (j+16)<= (nWidth - nStart); j+=16)

{

ushort16 line1 = convert_ushort16(vload16(0,pInLine+j-nStart+16));

vstore16(0,0,pOutLine+j) ;

}

b.另外发现使用*((__global uint4*)(pOutLine+j)) = as_uint4(result);比vstore16快，wait time 2.5ms.高通 80-N8592-1_L_OpenCL_Programming_Guide 中提到：

Vectorized load/store of a larger data type is more optimal than a small data type; e.g., a load of uint2* is more optimal than uchar8* .

For optimal SP to L2 bandwidth performance, align read access to a 32-bit address and write access to a 128-bit address.

c.原来写的内存没有对齐，使用*((__global uint4*)(pOutLine+j-5)) = as_uint4(result);wait time 1.9ms.

d.最后加上sum计算，采用的Horizontal kernel如下，localWorksize[] = {64};时时间最少，需要23ms,wait time 4.7ms , localWorksize = 128时,wait 6ms.

并且使用__attribute__((work_group_size_hint(64,1,1))) ,耗时22ms.

__kernel __attribute__((work_group_size_hint(,,)))

void ImageGaussianFilterHorizontal(__global const uchar* restrict source, // Source image

                        __global uchar* restrict  dest,  // Intermediate dest image

                                             const int imgWidth ,                // Image width

                                             const int imgHeight)

{

    const int y = get_global_id();

    if(y>=(imgHeight))

        return;

    const uchar m_nRightShiftNum = ;

    const uchar Rounding = ( << (m_nRightShiftNum - ));

    const uchar  m_nFilter[] = {,,,,,,,,,,};

    const int s = ;

    const int nStart = ;

    const int nWidth = imgWidth;

    __global const uchar* pInLine = source + y*nWidth;

    __global uchar* pOutLine = dest + y*nWidth;

    int j;

    uchar temp[];

    for(j = ; j < nStart; j ++)

    {

        ushort sum = ;

        for (int m = ; m<s / ; m++)

        {

            int k1 = (j + m - nStart);

            k1 = k1< ? -k1 : k1;

            int k2 = (j + nStart - m );

            sum += (pInLine[k1] + pInLine[k2])*m_nFilter[m];

        }

        sum += pInLine[j] * m_nFilter[s / ];

        sum = (sum + Rounding) >> ;

        temp[j] = (uchar)clamp(sum,(ushort),(ushort));

    }

    uchar16 result,pre_result;

    pre_result.sbcde = (uchar4)(temp[],temp[],temp[],temp[]);

    pre_result.sf = temp[];

    ushort16 line0 =  convert_ushort16(vload16(,pInLine+j-nStart));

    for ( ; (j+)<= (nWidth - nStart); j+=)

    {

        //prefetch(pInLine+j-nStart,32); //无变化

        ushort16 line1 =  convert_ushort16(vload16(,pInLine+j-nStart+));

        ushort16 temp0;

        ushort16 temp1;

        temp0 = line0;

        temp1.s0123 = line0.sabcd;

        temp1.s45 = line0.sef;

        temp1.s67 = line1.s01;

        temp1.s89abcdef = line1.s23456789;

        ushort16 sum =  ( temp0 + temp1 ) * m_nFilter[];

        temp0.s0123456789abcdef = temp0.s123456789abcdeff;

        temp0.sf = line1.s0;

        temp1.s0123456789abcdef = temp1.s00123456789abcde;

        temp1.s0 = line0.s9;

        sum += ( temp0 +  temp1 ) * m_nFilter[];

        temp0.s0123456789abcdef = temp0.s123456789abcdeff;

        temp0.sf = line1.s1;

        temp1.s0123456789abcdef = temp1.s00123456789abcde;

        temp1.s0 = line0.s8;

        sum += ( temp0 +  temp1 ) * m_nFilter[];

        temp0.s0123456789abcdef = temp0.s123456789abcdeff;

        temp0.sf = line1.s2;

        temp1.s0123456789abcdef = temp1.s00123456789abcde;

        temp1.s0 = line0.s7;

        sum += ( temp0 +  temp1 ) * m_nFilter[];

        temp0.s0123456789abcdef = temp0.s123456789abcdeff;

        temp0.sf = line1.s3;

        temp1.s0123456789abcdef = temp1.s00123456789abcde;

        temp1.s0 = line0.s6;

        sum += ( temp0 +  temp1 ) * m_nFilter[];

        temp0.s0123456789abcdef = temp0.s123456789abcdeff;

        temp0.sf = line1.s4;

        sum += ( temp0 ) * m_nFilter[];

        sum += (ushort)Rounding;

        line0 = line1;

        result.s0123 = pre_result.sbcde;

        result.s4 = pre_result.sf;

        pre_result = convert_uchar16(sum>>(ushort)) ;

        result.s5 = pre_result.s0;

        result.s67 = pre_result.s12;

        result.s89abcdef = pre_result.s3456789a;

        *( (__global uint4*)(pOutLine+j-) ) =  (as_uint4)(result) ;

    }

    *( (__global uint*)(pOutLine+j-) ) = (as_uint)(pre_result.sbcde);//last 5 bytes

    pOutLine[j-] = pre_result.sf;

    for( ; j < nWidth; j ++)

    {

        ushort sum = ;

        for (int m = ; m<s / ; m++)

        {

            int k1 = (j + m - nStart);

            int k2 = (j + nStart - m );

            k2 = k2 >= nWidth ?  * nWidth -  - k2 : k2;

            sum += (pInLine[k1] + pInLine[k2])*m_nFilter[m];

        }

        sum += pInLine[j] * m_nFilter[s / ];

        sum = (sum + Rounding) >> m_nRightShiftNum;

        pOutLine[j] =  (uchar)clamp(sum,(ushort),(ushort));

    }

}

相关文章