in src/UtilsAvx2.cc [16:171]
void transpose_avx2(
unsigned M,
unsigned N,
const float* src,
unsigned ld_src,
float* dst,
unsigned ld_dst) {
unsigned ib = 0, jb = 0;
if (N % 8 > 0 && N % 8 < 4) {
// If the remainder has n < 4 columns, we use the SSE kernel for the
// remainder because it requires 2 * (2 * 4 + 2 * N) = 16 + 4N instructions
// instead of 3 * 8 + 2 * N = 24 + 2N instructions in the masked AVX2
// kernel.
for (ib = 0; ib + 8 <= M; ib += 8) {
for (jb = 0; jb + 8 <= N; jb += 8) {
transpose_kernel_8x8_avx2(
&src[ib * ld_src + jb], ld_src, &dst[ib + jb * ld_dst], ld_dst);
}
for (unsigned i = ib; i < ib + 8; i += 4) {
transpose_kernel_mxn_sse<4>(
N - jb,
&src[i * ld_src + jb],
ld_src,
&dst[i + jb * ld_dst],
ld_dst);
}
}
} else if (N % 8 == 4) {
// If the remainder has 4 columns, we use the SSE kernel for the remainder
// because it requires 2 * 16 = 32 instructions instead of 3 * 8 + 2 * 4 =
// 32 instructions + looping overhead needed in the masked AVX2 kernel.
for (ib = 0; ib + 8 <= M; ib += 8) {
for (jb = 0; jb + 8 <= N; jb += 8) {
transpose_kernel_8x8_avx2(
&src[ib * ld_src + jb], ld_src, &dst[ib + jb * ld_dst], ld_dst);
}
for (unsigned i = ib; i < ib + 8; i += 4) {
transpose_kernel_4x4_sse(
&src[i * ld_src + jb], ld_src, &dst[i + jb * ld_dst], ld_dst);
}
}
} else {
for (ib = 0; ib + 8 <= M; ib += 8) {
for (jb = 0; jb + 8 <= N; jb += 8) {
transpose_kernel_8x8_avx2(
&src[ib * ld_src + jb], ld_src, &dst[ib + jb * ld_dst], ld_dst);
}
if (jb < N) {
transpose_kernel_mxn_avx2<8>(
N - jb,
&src[ib * ld_src + jb],
ld_src,
&dst[ib + jb * ld_dst],
ld_dst);
}
}
}
// Specialization for small M - ib cases so that the compiler can inline
// transpose_kernel_mxn_avx2 and unroll the loops whose iteration count
// depends on by M - ib .
// Specialization for m helps more than for n in transpose_kernel_mxn_avx2
// because we have more loops in that function whose iteration count depends
// on m.
switch (M - ib) {
case 1:
for (unsigned j = 0; j < N; ++j) {
dst[ib + j * ld_dst] = src[ib * ld_src + j];
}
break;
case 2:
for (jb = 0; jb + 4 <= N; jb += 4) {
transpose_kernel_mxn_sse<2>(
4, &src[ib * ld_src + jb], ld_src, &dst[ib + jb * ld_dst], ld_dst);
}
if (jb < N) {
transpose_kernel_mxn_sse<2>(
N - jb,
&src[ib * ld_src + jb],
ld_src,
&dst[ib + jb * ld_dst],
ld_dst);
}
break;
case 3:
for (jb = 0; jb + 4 <= N; jb += 4) {
transpose_kernel_mxn_sse<3>(
4, &src[ib * ld_src + jb], ld_src, &dst[ib + jb * ld_dst], ld_dst);
}
if (jb < N) {
transpose_kernel_mxn_sse<3>(
N - jb,
&src[ib * ld_src + jb],
ld_src,
&dst[ib + jb * ld_dst],
ld_dst);
}
break;
case 4:
for (jb = 0; jb + 4 <= N; jb += 4) {
transpose_kernel_4x4_sse(
&src[ib * ld_src + jb], ld_src, &dst[ib + jb * ld_dst], ld_dst);
}
if (jb < N) {
transpose_kernel_mxn_sse<4>(
N - jb,
&src[ib * ld_src + jb],
ld_src,
&dst[ib + jb * ld_dst],
ld_dst);
}
break;
case 5:
for (jb = 0; jb + 8 <= N; jb += 8) {
transpose_kernel_mxn_avx2<5>(
8, &src[ib * ld_src + jb], ld_src, &dst[ib + jb * ld_dst], ld_dst);
}
if (jb < N) {
transpose_kernel_mxn_avx2<5>(
N - jb,
&src[ib * ld_src + jb],
ld_src,
&dst[ib + jb * ld_dst],
ld_dst);
}
break;
case 6:
for (jb = 0; jb + 8 <= N; jb += 8) {
transpose_kernel_mxn_avx2<6>(
8, &src[ib * ld_src + jb], ld_src, &dst[ib + jb * ld_dst], ld_dst);
}
if (jb < N) {
transpose_kernel_mxn_avx2<6>(
N - jb,
&src[ib * ld_src + jb],
ld_src,
&dst[ib + jb * ld_dst],
ld_dst);
}
break;
case 7:
for (jb = 0; jb + 8 <= N; jb += 8) {
transpose_kernel_mxn_avx2<7>(
8, &src[ib * ld_src + jb], ld_src, &dst[ib + jb * ld_dst], ld_dst);
}
if (jb < N) {
transpose_kernel_mxn_avx2<7>(
N - jb,
&src[ib * ld_src + jb],
ld_src,
&dst[ib + jb * ld_dst],
ld_dst);
}
break;
}
}