- Removed some of the unrolling factors that affected the
performance of AVX2 DAXPYV kernel. In addition to improving
the current performance on sizes compatible to single-threaded
runs, this will now perform better for tiny sizes as well
since the overhead to reach the computation is less.
- Updated the vector partitioning logic, by using
bli_thread_range_sub( ... ), which ensures that there is no
false sharing among multiple threads.
- Updated the AOCL-DYNAMIC logic for the API, to include thresholds
or zen4 and zen5 micro-architectures.
AMD-Internal: [CPUPL-5514]
Change-Id: Iee9edddac685334213cd6694421ab3df3547e930
- Implemented a new front-end for the BLAS/CBLAS calls
to ?AXPBYV(BLAS-extension API), that is intended to
be compiled only on Zen micro-architectures(as per the
existing build system).
- This new front-end makes the framework lightweight for
BLAS/CBLAS calls to ?AXPBYV, by directly querying the
architecture ID and deploying the associated computational
kernel.
- Further updated the rerouting to other L1 kernels based
on alpha and beta value. This was initially present in
the Typed-API interface. It has been moved inside the
respective kernels, and only necessary rerouting is done
to specific L1 kernels to avoid redundant checks.
AMD-Internal: [CPUPL-5406]
Change-Id: I4af943d477a25dcdab4ee6009ad3dfa6a5c2b37e
- Implemented bli_zgemm_16x4_avx512_k1_nn( ... ) AVX512 kernel to
be used as part of BLAS/CBLAS calls to ZGEMM. The kernel is built
for handling the GEMM computation with inputs having k = 1,
with the transpose values being N(for column-major) and T(for
row-major).
- Updated the zgemm_blis_impl( ... ) layer to query the architecture
ID and invoke the AVX2 or AVX512 kernel accordingly.
- Added API level tests for accuracy and code-coverage, as well as
micro-kernel tests for verifying functionality and out-of-bounds
memory accesses.
AMD-Internal: [CPUPL-5249]
Change-Id: Id1f8bebff3e0da83c7febe86299564fd658b2e84
Use of AOCL_ENABLE_INSTRUCTIONS in dgemm tiny code path is
unnecessary and incorrectly caused AVX512 code to be run
on zen4 and later processors when AOCL_ENABLE_INSTRUCTIONS=avx2
or equivalent options was selected.
Replace with code to select kernel in a similar way to other
dgemm code paths and other APIs. Note that at present AVX2 code
is used the smallest matrix sizes on all zen platforms.
AMD-Internal: [CPUPL-5078]
Change-Id: Ie6b4895461cbbb915d2b48b92fc063f5cd6adb85
- Updated the existing code-path for ?AXPBYV to
reroute the inputs to the appropriate L1 kernel,
based on the alpha and beta value. This is done
in order to utilize sensible optimizations with
regards to the compute and memory operations.
- Updated the typed API interface for ?AXPBYV to include
an early exit condition(when n is 0, or when alpha is
0 and beta is 1). Further updated this layer to query
the right kernel from context, based on the input values
of alpha and beta.
- Added the necessary L1 vector kernels(i.e, ?SETV, ?ADDV,
?SCALV, ?SCAL2V and ?COPYV) to be used as part of special
case handling in ?AXPBYV.
- Moved the early return with negative increments from ?SCAL2V
kernels to its typed API interface.
- Updated the zen, zen2 and zen3 context to include function
pointers for all these vector kernels.
- Updated the existing ?AXPBYV vector kernels to handle only
the required computation. Additional cleanup was done to
these kernels.
- Added accuracy and memory tests for AVX2 kernels of ?SETV
?COPYV, ?ADDV, ?SCALV, ?SCAL2V, ?AXPYV and ?AXPBYV APIs
- Updated the existing thresholds in ?AXPBYV tests for complex
types. This is due to the fact that every complex multiplication
involves two mul ops and one add op. Further added test-cases
for API level accuracy check, that includes special cases of
alpha and beta.
- Decomposed the reference call to ?AXPBYV with several other
L1 BLAS APIs(in case of the reference not supporting its own
?AXPBYV API). The decomposition is done to match the exact
operations that is done in BLIS based on alpha and/or beta
values. This ensures that we test for our own compliance.
AMD-Internal: [CPUPL-4861]
Change-Id: Ia6d48f12f059f52b31c0bef6c75f47fd364952c6
- Updated the fused kernels (DOTXF and AXPYF) to properly handle cases
when b_n > fuse_factor.
- The fused kernels are expected to invoke respective Level-1 kernels
iteratively when b_n > fuse_factor.
AMD-Internal: [CPUPL-5246]
Change-Id: Ie7a0f4e61ede088663e3491269b3f1398d028095
BUG:
When alpha real and imaginary is zero
Output is computed as C= Beta * C + A * B instead of C = Beta * C
FIX:
Updated kernel to scale A * B product with alpha in case of alpha=0
Existing framework design:
- When alpha real and imaginary value is zero, framework handles to skip
kernel call to avoid alpha * A * B operation
- SCALM is invoked to perform Beta * C
- Accuracy issue was not observed as alpha=0 was handled in framework
- If we call kernel directly with alpha=0, results would be wrong
- Issue was figured out during microkernel testing using gtestsuite
AMD-Internal: [CPUPL-4454]
Change-Id: Ib6113f5226cd7c26a63781cdd20d35660f453803
- When n=1, reorder of B matrix is avoided to efficiently
process data. A dot-product based kernel is implemented to
perform gemv when n=1.
AMD-Internal: [SWLCSG-2354]
Change-Id: If5f74651ab11232d0b87d34bd05f65aacaea94f1
Export more symbols for BLIS kernels so that AOCL libFLAME
optimizations can call them directly.
AMD-Internal: [CPUPL-5044]
Change-Id: I45392b8a2a14ac2816141521b90b7ddb1216c733
AOCL libFLAME optimizations directly call some internal
BLIS symbols. Export them to enable this to work with
the BLIS shared library.
AMD-Internal: [CPUPL-5044]
Change-Id: Icb62dcb51e12d72dde8434593ab17de3c227c93d
- Kernel dimensions are 4x4.
- Two kernels are implemented, Right Upper and
Right lower.
- In case of Left variants of TRSM, transpose is
induced so that Right variant kernels can be used.
- No packing is performed in these kernels.
- Changes are made in the threshold to pick ZTRSM small
code path.
- BLIS_INLINE is removed from signature of
"TRSMSMALL_KER_PROT".
- These kernels do not support "ENABLE_TRSM_PREINVERSION".
- Newly added kernels do not support conjugate
transpose.
- Added multithreading to ZTRSM small code path.
AMD-Internal: [CPUPL-4324]
Change-Id: I683b1d5239593e54f433e7f27497d72dfbd9141c
- Implemented AVX512 kernels for handling the calls to ZGEMV
with no-transpose to A matrix.
- This includes the ZAXPYF, ZAXPYV and ZSETV kernels.
The set of ZAXPYF kernels include those with fuse-factor 8
(main kernel), 4 and 2(fringe kernels).
- Updated the bli_zgemv_unf_var2( ... ) function to set
the function pointers to these kernels, based on the
configuration. Further added the call to ZSETV at this
layer in case beta is 0.
AMD-Internal: [CPUPL-4974]
Change-Id: Iee4b724719e49023138bb16479765be44d677cd9
- In 2x1 fringe case in [RUN/RLT] kernel, 3 scomplex
precision numbers are being read instead of 1 scomplex.
- Fixed the code to read only one scomplex.
AMD-Internal: [CPUPL-4403]
Change-Id: If3ac03ed864618382d3a382a8cdff7ff8a94eb7d
Implement full support for zen5 as a separate BLIS sub-configuration
and code path within amdzen configuration family.
AMD-Internal: [CPUPL-3518]
Change-Id: Iaa5096e0b83bf0f0c3fd1c41e601ccd29bda3c09
Description:
1. Replaced aligned load intrinsics _mm512_load_ps
with unaligned load intrinsics _mm512_loadu_ps.
2. There is no guarantee that the memory address
can be aligned everywhere. The changes are under
beta multiplication. Copy paste error.
Change-Id: I978231b556e17ad7e66c5028ed1cd904c653e0a8
1. The 5 LOOP LPGEMM path is in-efficient when A or B is a vector
(i.e, m == 1 or n == 1).
2. An efficient implementation of lpgemv_rowvar_f32 is developed
considering the b matrix reorder in case of m=1 and post-ops fusion.
3. When m = 1 the algorithm divide the GEMM workload in n dimension
intelligently at a granularity of NR. Each thread work on A:1xk
B:kx(>=NR) and produce C=1x(>NR). K is unrolled by 4 along with
remainder loop.
4. When n = 1 the algorithm divide the GEMM workload in m dimension
intelligently at a granularity of MR. Each thread work on A:(>=MR)xk
B:kx1 and produce C = (>=MR)x1. When n=1 reordering of B is avoided
to efficiently process in n one kernel.
5. Fixed few warnings while loading 2 f32 bias elements using
_mm_load_sd using float pointer. Typecasted to (const double *)
AMD-Internal: [SWLCSG-2391, SWLCSG-2353]
Change-Id: If1d0b8d59e0278f5f16b499de1d629e63da5b599
-This post-operation computes C = (beta*C + alpha*A*B) + D, where D is
a matrix with dimensions and data type the same as that of C matrix.
AMD-Internal: [SWLCSG-2424]
Change-Id: I9464d1f514e3b04275fe93441489b4503a08937a
-This post-operation computes C = (beta*C + alpha*A*B) + D, where D is
a matrix with dimensions and data type the same as that of C matrix.
-For clang compilers (including aocc), -march=znver1 is not enabled for
zen kernels. Have updated CKVECFLAGS to capture the same.
AMD-Internal: [SWLCSG-2424]
Change-Id: Ie369f7ea5c80ab69eea3f3e03a8d9546e14f5c09
- In 3x1 fringe case in [RLN/RUT] kernel, 4 double
precision floats are being read instead of 3 doubles.
- Fixed the code to read only 3 double.
AMD-Internal: [CPUPL-4403]
Change-Id: If0afb155efefabe13487cf322d479981f1838aa2
1. Added Trans A feature to handle column major inputs
for A matrix.
2. Trans A is enabled by on-the-go pack of A matrix.
3. The on-the-go pack of A converts a column storage
MCxKC block of A into row storage MCxKC block as
LPGEMM kernels are row major kernels.
4. New pack routines are added for conversion of A matrix
from column major storage to row major storage.
5. LPGEMM Cntx is updated with pack kernel function
pointers.
6. Packing of A matrix:
- Converts column major input A to row major
in blocks of MCxKC with newly added pack A
functions when cs_a > 1.
7. Pack routines are added for AVX512 and AVX2
INT8 LPGEMM APIs.
8. Trans A feature is now supported in:
1. u8s8s32os32/os8
2. u8s8s16os16/os8/ou8
3. s8s8s32os32/os8
4. s8s8s16os16/os8
AMD-Internal: SWLCSG-2582
Change-Id: I7ce331545525a9a09f3853280615b55fcf2edabf
-As it stands, in LPGEMM, users are expected to pass an array of values
with length the same as N dimension as inputs for zero point or scale
factor. However at times, a single scalar value is used as zero point
or scale factor for the entire downscaling operation. The mandate to
pass an array requires the user to allocate extra memory and fill it
with the scalar value so as to be used in downscaling. This limitation
is lifted as part of this commit, and now scalar values can be passed
as zero point or scale factor.
-LPGEMM bench enhancements along with new input format to improve
readability as well as flexibility.
AMD-Internal: [SWLCSG-2581]
Change-Id: Ibd0d89f03e1acadd099382dffcabfec324ceb50f
Some text files were missing a newline at the end of the file.
One has been added.
AMD-Internal: [CPUPL-3519]
Change-Id: I4b00876b1230b036723d6b56755c6ca844a7ffce
1. Prefetch only MR rows or rows required for fringe cases
2. Specify prefetching offset - the least column address supported
by masked functions
3. Removed unnecessary prefetches in fringe case for mx4 kernels
Updated gtestuite for sgemm calls
AMD_Internal: [CPUPL-4221]
Change-Id: I1e2e7d3ebce37dc54a2f0a5c1c70ce0a6d4c8d6c
- This commit uses avx2 and avx512 masked load instructions
for handling edge case where vector size is not exact multiple
of avx2/avx512 vector register size.
- Thanks to Shubham, Sharma <shubham.sharma3@amd.com> for
avx512 ddotv kernel changes
Change-Id: I998651eeb1083caf3308f1b45bd7d55b7974bcb4
Segfault was reported through nightly jenkins job.
Issue was observed when running in MT mode.
Issue was due to extra broadcast being used.
Extra broadcast would access out of bound memory on input buffer
Cleaned up cobbler list by removing unused registers.
AMD_Internal: [CPUPL-4180]
Change-Id: I1c8715b2850ef855328f2ef12f215987299bdb2b
Added all fringe kernels with mask load store support
Fringe kernels cover m direction from 5 to 1 and
n direction from 15 to 1 for row storage format
- New edge kernels that uses masked load-store
instructions for handling corner cases.
- Mask load-store instruction macros are added.
vmaskmovps, VMASKMOVPS for masked load-store.
- It improves performance by reducing branching overhead
and by being more cache friendly.
- Mask load-store is added only for row storage format
AMD-Internal: [CPUPL-4041]
Change-Id: I563c036c79bf8e476a8ebde37f8f6db751fb3456
- This commit helps improving performance for very small input
by reducing framework check and routing all such inputs to
bli_dgemm_tiny_6x8_kernel. It forces single threaded computation
for such sizes.
- It invokes bli_dgemm_tiny_6x8_kernel for ZEN, ZEN2, ZEN3 and ZEN4
code path. Except for the case AOCL_ENABLE_INSTRUCTIONS environment
variable is set to avx512. In that case, such a small inputs are
routed to bli_dgemm_tiny_24x8_kernel avx512 kernel.
AMD-Internal: [CPUPL-1701]
Change-Id: Idf59f4a8ee76ee8f2514a33be2b618e3ce02383e
- Abstracted packing from the vectorized kernels for SNRM2 and SCNRM2 to
a layer higher.
- Added a scalar loop to handle compute in case of non-unit strides.
This loop ensures functionality in case packing fails at the
framework level.
AMD-Internal: [CPUPL-3633]
Change-Id: I555aea519d7434d43c541bb0f661f81105135b98
- Updated the final reduction of partial sums( AVX-2 code section )
to use scalar accumulation entirely, instead of using the
_mm256_hadd_pd( ... ) intrinsic. This will in turn change the
associativity in the reduction step.
- Reverted to using scalar code on the fringe cases in AVX-2 kernel
for DNRM2 and DZNRM2, for improving functional correctness.
AMD-Internal: [CPUPL-4049]
Change-Id: I9d320b39d23a0cbcc77fb24d951fced778ea5ea5
- This commit implements avx512 dgemm kernel for k=1 cases.
which gets called for zen4 codepath.
- Added architecture check for k=1 kernel in dgemm code path
to pick correct kernel based on cpu arhcitecture since now
blis is having avx2 and avx512 dgemm kernels for k=1 case.
- Previously in dgemm path bli_dgemm_8x6_avx2_k1_nn kernel was
being called irrespective of architecture type.
- Added architecture check before calling the kernel for case where
k=1, so only for respective architectures this kernel is invoked.
AMD-Internal: [CPUPL-4017]
Change-Id: I418bbc933b41db41d323b331c6d89893868a6971
- The call to the bli_saxpyf_zen_int_6( ... ) is explicitly
present in the bli_gemv_unf_var2_amd.c file, as part of the
bli_sgemv_unf_var2( ... ) function. This was changed to
bli_saxpyf_zen_int_5( ... )( thereby changing the fuse factor
from 6 to 5 ), in accordance to the function pointer present
in the zen3 and zen4 context files.
- Changed the accumulator type to double from float, inside the
fringe loop for unit-strides(vectorized path) and non-unit strides
(scalar code).
AMD-Internal: [CPUPL-4028]
Change-Id: Iab1a0318f461cba9a7041093c6865ae8396d231e
-The zero point data type is different based on the downscale data
type. For int8_t downscale type, zero point type is int8_t whereas for
uint8_t downscale type, it is uint8_t. During downscale post-op, the
micro-kernels upscales the zero point from its data type (int8_t or
uint8_t) to that of the accumulation data type and then performs the
zero point addition. The accumulated output is then stored as downscaled
type in a later storage phase. For the <u|s>8s8s16 micro-kernels, the
upscaling to int16_t (accumulation type) is always performed assuming
the zero point is int8_t using the _mm256_cvtepi8_epi16 instruction.
However this will result in incorrect upscaled zero point values if the
downscale type is uint8_t and the associated zero point type is also
uint8_t. This issue is corrected by switching between the correct
upscale instruction based on the zero point type.
AMD-Internal: [SWLCSG-2500]
Change-Id: I92eed4aed686c447d29312836b9e551d6dd4b076
Description:
1. Updated ERF function threshold from 3.91920590400 to 3.553
to match with the reference erf float implementation which
reduced errors a the borders and also clipped the output
to 1.0
2. Updated packa function call with pack function ptr in bf16
api to avoid compilation issues for non avx512bf16 archs
3. Updated lpgemm bench
[AMD-Internal: SWLCSG-2423 ]
Change-Id: Id432c0669521285e6e6a151739d9a72a7340381d
- For k > KC, C matrix is getting scaled by beta on each
iteration. It should be scaled only once. Fixed the scaling
of C matrix by beta in K loop.
- Corrected A and B matrix buffer offsets, for cases where k > KC.
AMD-Internal: [CPUPL-4078]
AMD-Internal: [CPUPL-4079]
AMD-Internal: [CPUPL-4081]
AMD-Internal: [CPUPL-4080]
AMD-Internal: [CPUPL-4087]
Change-Id: I27f426caf48e094fd75f1f719acb4ac37d9daeaa
- Updated the bli_dnormfv_unb_var1( ... ) and
bli_znormfv_unb_var1( ... ) function to support
multithreaded calls to the respective computational
kernels, if and when the OpenMP support is enabled.
- Added the logic to distribute the job among the threads such
that only one thread has to deal with fringe case(if required).
The remaining threads will execute only the AVX-2 code section
of the computational kernel.
- Added reduction logic post parallel region, to handle overflow
and/or underflow conditions as per the mandate. The reduction
for both the APIs involve calling the vectorized kernel of
dnormfv operation.
- Added changes to the kernel to have the scaling factors and
thresholds prebroadcasted onto the registers, instead of
broadcasting every time on a need basis.
- Non-unit stride cases are packed to be redirected to the
vectorized implementation. In case the packing fails, the
input is handled by the fringe case loop in the kernel.
- Added the SSE implementation in bli_dnorm2fv_unb_var1_avx2( ... )
and bli_dznorm2fv_unb_var1_avx2( ... ) kernels, to handle fringe
cases of size = 2 ( and ) size = 1 or non-unit strides respectively.
AMD-Internal: [CPUPL-3916][CPUPL-3633]
Change-Id: Ib9131568d4c048b7e5f2b82526145622a5e8f93d
- This commit focused on enhancing the performance of dgemm
for matrices for very small dimenstions.
- blis_dgemm_tiny function re-uses dgemm sup kernels, bypassing
the conventional SUP framework code path. As SUP framework code path
requires the creation and initilization of blis objects,
accessing all the needed meta-information from objects, querying contexts
which adds performance penaulty while computing for matrices with very
small dimensions.
- To avoid such performance penaulty blis_dgemm_tiny function implements
a lightweight support code so that it can re-use dgemm SUP kernels such a way
that it directly operates on input buffers. It avoids framework overhead of
creating and intializing blis objects, context intialization, accessing other
large framework data structures.
- blis_dgemm_tiny function checks for threshold condition to match before
picking the kernel. For zen, zen2, zen3 architecture tiny kernel is invoked
for any shape as long as m < 8 and k <= 1500 or m < 1000 and n <= 24 and k <=1500.
While for zen4 as long as dimensions are less than 1500 for m,n,k tiny kernel is
invoked.
-blis_dgemm_tiny function supports single threaded computation as of now.
AMD-Internal: [CPUPL-3574]
Change-Id: Ife66d35b51add4fccbeebd29911e0c957e59a05f
- Added 2x6 ZGEMM row-preferred kernel.
- Kernel supports prefetch_a, prefetch_b,
prefetch_a_next and prefetch_b_next.
- Multiple Ways to prefetch c are supported.
- prefetch_a and prefetch_c are enabled by
default.
- K loop is divided into multiple subloops for
better c prefetch.
- Added 2x6 ZTRSM row-preferred lower
and upper kernels using AVX2 ISA.
- These kernels are used for ZTRSM only, zgemm
still uses 3x4 kernel.
- Kernels support row/col/gen storage.
- Updated the zen3 and zen4 config to enable
use of these kernels for TRSM in zen3 and
zen4 path.
- Updated CMakeLists.txt with ZGEMM kernels for
windows build.
AMD-Internal: [CPUPL-3781]
Change-Id: I236205f63a7f6b60bf1a5127a677d27425511e73
- Added an explicit function definition for ZGEMV var 1. This
removes the need to query the context for Zen architectures.
- Added a new INSERT_GENTFUNC to generate the definition only
for scomplex type.
- Rewrote ZDOTXF kernel and added the function name for ZDOTV
instead of querying it.
- With this change fringe loop is vectorized using SSE
instructions.
AMD-Internal:[CPUPL-3997]
Change-Id: I790214d528f9e39f63387bc95bf611f84d3faca3
Downscaling is used when GEMM output is accumulated at a higher
precision and needs to be converted to a lower precision afterwards.
Currently the u8s8s16 flavor of api only supports downscaling to s8
(int8_t) via aocl_gemm_u8s8s16os8 after results are accumulated at
int16_t.
LPGEMM is modified to support downscaling to different data types,
like u8, s16, apart from s8. The framework (5 loop) passes the
downscale data type to the micro-kernels. Within the micro-kernel,
based on the downscale type, appropriate beta scaling and output
buffer store logic is executed. This support is only enabled for
u8s8s16 flavor of api's.
The LPGEMM bench is also modified to support passing downscale data
type for performance and accuracy testing.
AMD-Internal: [SWLCSG-2313]
Change-Id: I723d0802baf8649e5e41236b239880a6043bfd30
- Updated the bli_zaxpbyv_zen_int( ... ) kernel's computational
logic. The kernel performs two different sets of compute based
on the value of alpha, for both unit and non-unit strides. There
are no constraints on beta scaling of the 'y' vector.
- Updated the logic to support 'x' conjugate in the computation.
The kernel supports conjugate/no conjugate operation through the
usage of _mm256_fmsubadd_pd( ... ) and _mm256_addsub_pd( ... )
intrinsics.
- Updated the early return condition in the kernel to adhere to
the standard compliance.
- Updated the scalar computation with vector computation(using 128
bit registers), in case of dealing with a single element(fringe case)
in unit-stride or vectors with non-unit strides. A single dcomplex
element occupies 128 bits in memory, thereby providing scope for
this optimization.
- Added accuracy and extreme value testing with sufficient sizes
and initializations, to test the required main and fringe cases
of the computation.
AMD-Internal: [CPUPL-3623]
Change-Id: I7ae918856e7aba49424162290f3e3d592c244826
Description:
The expf_max and expf_min have more precission than
the computation which is leading to corss the clipping at
the edge case which is causing nan's in the tanh output.
Updated the thresholds to less precission to clip the
edge cases to avoid nan's in the tanh output.
AMD-Internal: [SWLCSG-2423 ]
Change-Id: I25a665475692f47443f30ca5dd09e8e06a0bfe29
-Downscaled / quantized value is calculated using the formula
x' = (x / scale_factor) + zero_point. As it stands, the micro-kernels
for these APIs only support scaling.
Zero point addition is implemented as part of this commit, with it
being fused as part of the downscale post-op in the micro-kernel. The
zero point input is a vector of int8 values, and currently only vector
based zero point addition is supported.
-Bench enhancements to test/benchmark zero point addition.
AMD-Internal: [SWLCSG-2332]
Change-Id: I96b4b1e5a384a4683b50ca310dcfb63debb1ebea
* commit 'b683d01b':
Use extra #undef when including ba/ex API headers.
Minor preprocessor/header cleanup.
Fixed typo in cpp guard in bli_util_ft.h.
Defined eqsc, eqv, eqm to test object equality.
Defined setijv, getijv to set/get vector elements.
Minor API breakage in bli_pack API.
Add err_t* "return" parameter to malloc functions.
Always stay initialized after BLAS compat calls.
Renamed membrk files/vars/functions to pba.
Switch allocator mutexes to static initialization.
AMD-Internal: [CPUPL-2698]
Change-Id: Ied2ca8619f144d4b8a7123ac45a1be0dda3875df
- In GEMV variant 1, the input matrix A is in row major. X vector
has to be of unit stride if the operation is to be vectorized.
- In cases when X vector is non-unit stride, vectorization of the GEMV
operation inside the kernel has been ensured by packing the input X
vector to a temporary buffer with unit stride. Currently, the
packing is done using the SCAL2V.
- In case of DGEMV, X vector is scaled by alpha as part of packing.
In CGEMV and ZGEMV, alpha is passed as 1 while packing.
- The temporary buffer created is released once the GEMV operation
is complete.
- In DGEMV variant 1, moved problem decomposition for Zen architecture
to the DOTXF kernel.
- Removed flag check based kernel dispatch logic from DGEMV. Now,
kernels will be picked from the context for non-avx machines. For
avx machines, the kernel(s) to be dispatched is(are) assigned to
the function pointer in the unf_var layer.
AMD-Internal: [CPUPL-3475]
Change-Id: Icd9fd91eccd831f1fcb9fbf0037fcbbc2e34268e
- In variant 2 of GEMV, A matrix is in column major. Y vector has
to be of unit stride if the operation is to be vectorized.
- In cases when Y vector is non-unit stride, vectorization of the
GEMV operation inside the kernel has been ensured by packing the
input Y vector to a temporary buffer with unit stride. As part of
the packing Y is scaled by beta to reduce the number of times Y
vector is to be loaded.
- After performing the GEMV operation, the results in the temporary
buffer are copied to the original buffer and the temporary one is
released.
- In DGEMV var 2, moved problem decomposition for Zen architecture
to the AXPYF kernel.
- Removed flag check based kernel dispatch logic from DGEMV. Now,
kernels will be picked from the context for non-avx machines. For
avx machines, the kernel(s) to be dispatched is(are) assigned to
the function pointer in the unf_var layer.
AMD-Internal: [CPUPL-3485]
Change-Id: I7b2efb00a9fa9abca65abca07ee80f38229bf654
