SME1 based direct kernel (with alpha and beta) for cblas_sgemm level 3

Setting up all the infrastructure for BGEMM support in OpenBLAS, hopefully I found all the right places.

Derived mostly from the previous work done in https://github.com/OpenMathLib/OpenBLAS/pull/5287

Co-authored-by: Ye Tao <ye.tao@arm.com>

make SMALL_MATRIX_OPT=1

Move "direct SGEMM" functionality out of the SkylakeX SGEMM kernel and make it available
(on x86_64 targets only for now) in DYNAMIC_ARCH builds
* Add  sgemm_direct targets in the kernel Makefile.L3 and CMakeLists.txt
* Add direct_sgemm functions to the gotoblas struct in common_param.h
* Move sgemm_direct_performant helper to separate file
* Update gemm.c  to macros for sgemm_direct to support dynamic_arch naming via common_s,h
* (Conditionally) add sgemm_direct functions in setparam-ref.c

This patch adds support for bfloat16 data type matrix multiplication kernel.
For architectures that don't support bfloat16, it is defined as unsigned short
(2 bytes).  Default unroll sizes can be changed as per architecture as done for
SGEMM and for now 8 and 4 are used for M and N.  Size of ncopy/tcopy can be
changed as per architecture requirement and for now, size 2 is used.

Added shgemm in kernel/power/KERNEL.POWER9 and tested in powerpc64le and
powerpc64.  For reference, added a small test compare_sgemm_shgemm.c to compare
sgemm and shgemm output.

This patch does not cover OpenBLAS test, benchmark and lapack tests for shgemm.
Complex type implementation can be discussed and added once this is approved.

OpenBLAS has a fancy algorithm for copying the input data while laying
it out in a more CPU friendly memory layout.

This is great for large matrixes; the cost of the copy is easily
ammortized by the gains from the better memory layout.

But for small matrixes (on CPUs that can do efficient unaligned loads) this
copy can be a net loss.

This patch adds (for SKYLAKEX initially) a "sgemm direct" mode, that bypasses
the whole copy machinary for ALPHA=1/BETA=0/... standard arguments,
for small matrixes only.

What is small? For the non-threaded case this has been measured to be
in the M*N*K = 28 * 512 * 512 range, while in the threaded case it's
less, around M*N*K = 1 * 512 * 512

The Ximatcopy functions create a copy of the input matrix
although they seem to work inplace. The new routines
XIMATCOPY_K_YY perform the operations inplace if the leading
dimension does not change.

Signed-off-by: Timothy Gu <timothygu99@gmail.com>