Apply our new GEMM kernel implementation, written in C with vector intrinsics,
also for DGEMM and DTRMM on Z14 and newer (i.e., architectures with FP32 SIMD
instructions). As a result, we gain around 10% in performance on z15, in
addition to improving maintainability.
Signed-off-by: Marius Hillenbrand <mhillen@linux.ibm.com>
... since it gains another ~2% of SGEMM and DGEMM performance on z15;
also, the code just called for that cleanup.
Signed-off-by: Marius Hillenbrand <mhillen@linux.ibm.com>
Introduce inline assembly so that we can employ vector loads with
alignment hints on older compilers (pre gcc-9), since these are still
used in distributions such as RHEL 8 and Ubuntu 18.04 LTS.
Informing the hardware about alignment can speed up vector loads. For
that purpose, we can encode hints about 8-byte or 16-byte alignment of
the memory operand into the opcodes. gcc-9 and newer automatically emit
such hints, where applicable. Add a bit of inline assembly that achieves
the same for older compilers. Since an older binutils may not know about
the additional operand for the hints, we explicitly encode the opcode in
hex.
Signed-off-by: Marius Hillenbrand <mhillen@linux.ibm.com>
Change register blocking for SGEMM (and STRMM) on z14 from 8x4 to 16x4
by adjusting SGEMM_DEFAULT_UNROLL_M and choosing the appropriate copy
implementations. Actually make KERNEL.Z14 more flexible, so that the
change in param.h suffices. As a result, performance for SGEMM improves
by around 30% on z15.
On z14, FP SIMD instructions can operate on float-sized scalars in
vector registers, while z13 could do that for double-sized scalars only.
Thus, we can double the amount of elements of C that are held in
registers in an SGEMM kernel.
Signed-off-by: Marius Hillenbrand <mhillen@linux.ibm.com>
Employ the newly added GEMM kernel also for STRMM on Z14. The
implementation in C with vector intrinsics exploits FP32 SIMD operations
and thereby gains performance over the existing assembly code. Extend
the implementation for handling triangular matrix multiplication,
accordingly. As added benefit, the more flexible C code enables us to
adjust register blocking in the subsequent commit.
Tested via make -C test / ctest / utest and by a couple of additional
unit tests that exercise blocking.
Signed-off-by: Marius Hillenbrand <mhillen@linux.ibm.com>
Add a new GEMM kernel implementation to exploit the FP32 SIMD
operations introduced with z14 and employ it for SGEMM on z14 and newer
architectures.
The SIMD extensions introduced with z13 support operations on
double-sized scalars in vector registers. Thus, the existing SGEMM code
would extend floats to doubles before operating on them. z14 extended
SIMD support to operations on 32-bit floats. By employing these
instructions, we can operate on twice the number of scalars per
instruction (four floats in each vector registers) and avoid the
conversion operations.
The code is written in C with explicit vectorization. In experiments,
this kernel improves performance on z14 and z15 by around 2x over the
current implementation in assembly. The flexibilty of the C code paves
the way for adjustments in subsequent commits.
Tested via make -C test / ctest / utest and by a couple of additional
unit tests that exercise blocking (e.g., partial register blocks with
fewer than UNROLL_M rows and/or fewer than UNROLL_N columns).
Signed-off-by: Marius Hillenbrand <mhillen@linux.ibm.com>
The ZARCH implementations of ?sum contain a cut & paste-error: An inline
assembly argument is named "sum", but the assembly references "asum"
instead. The mismatch causes a build error. This is fixed.
Marius Hillenbrand
int_13h
Andreas Arnez
Martin Kroeker
maamountki
QWR QWR