Vector Class Discussion

I've discovered that the following vectorclass-based code, which transposes a 4x16 matrix of unsigned short integers, performs poorly if compiled for AVX2 instruction set compared to AVX for the same CPU. For example, on the Intel Core i5-4460 (Haswell) CPU (actually on the same machine), the code compiled with gcc -march=native or -march=corei7-avx2 is three times slower than itself compiled with -march=corei7-avx.

Something must be wrong there, but I don't know where. It's microarchitecture, compiler, or library (vectorclass) issue?

typedef  Vec16us X16T;
inline void transpi (X16T y[4], X16T const x[4])

{    // transpose a 4x16 matrix x into a 16x4 matrix y

    X16T x0,x1,x2,x3,y0,y1,y2,y3;

    x0.load( &x[0] );

    x1.load( &x[1] );

    x2.load( &x[2] );

    x3.load( &x[3] );

    y0 = blend16us< 0,16, 1,17, 2,18, 3,19, 4,20, 5,21, 6,22, 7,23>(x0,x2);

    y1 = blend16us< 8,24, 9,25,10,26,11,27,12,28,13,29,14,30,15,31>(x0,x2);

    y2 = blend16us< 0,16, 1,17, 2,18, 3,19, 4,20, 5,21, 6,22, 7,23>(x1,x3);

    y3 = blend16us< 8,24, 9,25,10,26,11,27,12,28,13,29,14,30,15,31>(x1,x3);

    x0 = blend16us< 0,16, 1,17, 2,18, 3,19, 4,20, 5,21, 6,22, 7,23>(y0,y2);

    x1 = blend16us< 8,24, 9,25,10,26,11,27,12,28,13,29,14,30,15,31>(y0,y2);

    x2 = blend16us< 0,16, 1,17, 2,18, 3,19, 4,20, 5,21, 6,22, 7,23>(y1,y3);

    x3 = blend16us< 8,24, 9,25,10,26,11,27,12,28,13,29,14,30,15,31>(y1,y3);

    x0.stor( &y[0] );

    x1.stor( &y[1] );

    x2.stor( &y[2] );

    x3.stor( &y[3] );

}

Hello,

I've discovered that the following vectorclass-based code, which transposes a 4x16 matrix of unsigned short integers, performs poorly if compiled for AVX2 instruction set compared to AVX for the same CPU.

The AVX2 instruction set uses 256-bit integer vectors and general shuffle instructions, which happens to be less efficient in your case. The blend16s and permute16s functions in the file vectori256.h has plenty of special cases, but unfortunately not a special case for the unpack instructions.