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] );

}