s390x: Add vectorized sgemm kernel for Z14 and newer
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>
This commit is contained in:
Marius Hillenbrand
parent
d7c1677c20
commit
43c0d4f312
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@ -5,6 +5,6 @@ FCOMMON_OPT += -march=z13 -mzvector
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endif
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ifeq ($(CORE), Z14)
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CCOMMON_OPT += -march=z14 -mzvector
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CCOMMON_OPT += -march=z14 -mzvector -O3
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FCOMMON_OPT += -march=z14 -mzvector
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endif
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@ -91,7 +91,7 @@ DTRMMKERNEL = trmm8x4V.S
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CTRMMKERNEL = ctrmm4x4V.S
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ZTRMMKERNEL = ztrmm4x4V.S
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SGEMMKERNEL = strmm8x4V.S
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SGEMMKERNEL = gemm_vec.c
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SGEMMINCOPY = ../generic/gemm_ncopy_8.c
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SGEMMITCOPY = ../generic/gemm_tcopy_8.c
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SGEMMONCOPY = ../generic/gemm_ncopy_4.c
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@ -102,7 +102,7 @@ SGEMMONCOPYOBJ = sgemm_oncopy$(TSUFFIX).$(SUFFIX)
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SGEMMOTCOPYOBJ = sgemm_otcopy$(TSUFFIX).$(SUFFIX)
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DGEMMKERNEL = gemm8x4V.S
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DGEMMINCOPY = ../generic/gemm_ncopy_8.c
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DGEMMITCOPY = ../generic/gemm_tcopy_8.c
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@ -0,0 +1,342 @@
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/*
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* Copyright (c) IBM Corporation 2020.
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* All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions are
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* met:
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*
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* 1. Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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*
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* 2. Redistributions in binary form must reproduce the above copyright
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* notice, this list of conditions and the following disclaimer in
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* the documentation and/or other materials provided with the
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* distribution.
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* 3. Neither the name of the OpenBLAS project nor the names of
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* its contributors may be used to endorse or promote products
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* derived from this software without specific prior written
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* permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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* ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
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* LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE
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* USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*/
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#include "common.h"
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#include <vecintrin.h>
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#include <stdbool.h>
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#include <stdio.h>
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#include <stdlib.h>
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#ifdef COMPLEX
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#error "Handling for complex numbers is not supported in this kernel"
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#endif
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#ifdef DOUBLE
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#define UNROLL_M DGEMM_DEFAULT_UNROLL_M
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#define UNROLL_N DGEMM_DEFAULT_UNROLL_N
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#else
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#define UNROLL_M SGEMM_DEFAULT_UNROLL_M
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#define UNROLL_N SGEMM_DEFAULT_UNROLL_N
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#endif
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static const size_t unroll_m = UNROLL_M;
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static const size_t unroll_n = UNROLL_N;
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/*
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* Background:
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*
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* The algorithm of GotoBLAS / OpenBLAS breaks down the matrix multiplication
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* problem by splitting all matrices into partitions multiple times, so that the
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* submatrices fit into the L1 or L2 caches. As a result, each multiplication of
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* submatrices can stream data fast from L1 and L2 caches. Inbetween, it copies
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* and rearranges the submatrices to enable contiguous memory accesses to
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* improve locality in both caches and TLBs.
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*
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* At the heart of the algorithm is this kernel, which multiplies, a "Block
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* matrix" A (small dimensions) with a "Panel matrix" B (number of rows is
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* small) and adds the result into a "Panel matrix" C; GotoBLAS calls this
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* operation GEBP. This kernel further partitions GEBP twice, such that (1)
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* submatrices of C and B fit into the L1 caches (GEBP_column_block) and (2) a
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* block of C fits into the registers, while multiplying panels from A and B
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* streamed from the L2 and L1 cache, respectively (GEBP_block).
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*
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*
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* Algorithm GEBP(A, B, C, m, n, k, alpha):
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*
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* The problem is calculating C += alpha * (A * B)
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* C is an m x n matrix, A is an m x k matrix, B is an k x n matrix.
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*
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* - C is in column-major-order, with an offset of ldc to the element in the
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* next column (same row).
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* - A is in row-major-order yet stores SGEMM_UNROLL_M elements of each column
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* contiguously while walking along rows.
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* - B is in column-major-order but packs SGEMM_UNROLL_N elements of a row
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* contiguously.
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* If the numbers of rows and columns are not multiples of SGEMM_UNROLL_M or
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* SGEMM_UNROLL_N, the remaining elements are arranged in blocks with power-of-2
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* dimensions (e.g., 5 remaining columns would be in a block-of-4 and a
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* block-of-1).
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*
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* Note that packing A and B into that form is taken care of by the caller in
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* driver/level3/level3.c (actually done by "copy kernels").
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*
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* Steps:
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* - Partition C and B into blocks of n_r (SGEMM_UNROLL_N) columns, C_j and B_j.
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* Now, B_j should fit into the L1 cache.
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* - For each partition, calculate C_j += alpha * (A * B_j) by
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* (1) Calculate C_aux := A * B_j (see below)
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* (2) unpack C_j = C_j + alpha * C_aux
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*
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*
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* Algorithm for Calculating C_aux:
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*
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* - Further partition C_aux and A into groups of m_r (SGEMM_UNROLL_M) rows,
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* such that the m_r x n_r-submatrix of C_aux can be held in registers. Each
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* submatrix of C_aux can be calculated independently, and the registers are
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* added back into C_j.
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*
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* - For each row-block of C_aux:
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* (uses a row block of A and full B_j)
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* - stream over all columns of A, multiply with elements from B and
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* accumulate in registers. (use different inner-kernels to exploit
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* vectorization for varying block sizes)
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* - add alpha * row block of C_aux back into C_j.
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*
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* Reference:
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*
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* The summary above is based on staring at various kernel implementations and:
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* K. Goto and R. A. Van de Geijn, Anatomy of High-Performance Matrix
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* Multiplication, in ACM Transactions of Mathematical Software, Vol. 34, No.
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* 3, May 2008.
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*/
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#define VLEN_BYTES 16
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#define VLEN_FLOATS (VLEN_BYTES / sizeof(FLOAT))
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typedef FLOAT vector_float __attribute__ ((vector_size (16)));
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/**
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* Calculate for a row-block in C_i of size ROWSxCOLS using vector intrinsics.
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*
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* @param[in] A Pointer current block of input matrix A.
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* @param[in] k Number of columns in A.
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* @param[in] B Pointer current block of input matrix B.
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* @param[inout] C Pointer current block of output matrix C.
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* @param[in] ldc Offset between elements in adjacent columns in C.
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* @param[in] alpha Scalar factor.
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*/
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#define VECTOR_BLOCK(ROWS, COLS) \
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static inline void GEBP_block_##ROWS##_##COLS( \
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FLOAT const *restrict A, BLASLONG bk, FLOAT const *restrict B, \
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FLOAT *restrict C, BLASLONG ldc, FLOAT alpha) { \
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_Static_assert( \
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ROWS % VLEN_FLOATS == 0, \
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"rows in block must be multiples of vector length"); \
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vector_float Caux[ROWS / VLEN_FLOATS][COLS]; \
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\
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for (BLASLONG i = 0; i < ROWS / VLEN_FLOATS; i++) \
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for (BLASLONG j = 0; j < COLS; j++) \
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Caux[i][j] = vec_splats(ZERO); \
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\
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/* \
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* Stream over the row-block of A, which is packed \
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* column-by-column, multiply by coefficients in B and add up \
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* into temporaries Caux (which the compiler will hold in \
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* registers). Vectorization: Multiply column vectors from A \
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* with scalars from B and add up in column vectors of Caux. \
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* That equates to unrolling the loop over rows (in i) and \
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* executing each unrolled iteration as a vector element. \
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*/ \
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for (BLASLONG k = 0; k < bk; k++) { \
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for (BLASLONG i = 0; i < ROWS / VLEN_FLOATS; i++) { \
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vector_float Ak = \
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*(vector_float *)(A + i * VLEN_FLOATS + \
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k * ROWS); \
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\
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for (BLASLONG j = 0; j < COLS; j++) \
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Caux[i][j] += Ak * B[j + k * COLS]; \
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} \
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} \
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\
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/* \
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* Unpack row-block of C_aux into outer C_i, multiply by \
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* alpha and add up. \
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*/ \
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for (BLASLONG j = 0; j < COLS; j++) { \
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for (BLASLONG i = 0; i < ROWS / VLEN_FLOATS; i++) { \
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vector_float *C_ij = \
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(vector_float *)(C + i * VLEN_FLOATS + \
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j * ldc); \
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*C_ij += alpha * Caux[i][j]; \
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} \
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} \
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}
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VECTOR_BLOCK(8, 4)
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VECTOR_BLOCK(8, 2)
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VECTOR_BLOCK(8, 1)
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VECTOR_BLOCK(4, 4)
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VECTOR_BLOCK(4, 2)
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VECTOR_BLOCK(4, 1)
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#ifdef DOUBLE
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VECTOR_BLOCK(2, 4)
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VECTOR_BLOCK(2, 2)
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#endif
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/**
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* Handle calculation for row blocks in C_i of any size by dispatching into
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* macro-defined (inline) functions or by deferring to a simple generic
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* implementation. Note that the compiler can remove this awkward-looking
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* dispatching code while inlineing.
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*
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* @param[in] m Number of rows in block C_i.
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* @param[in] n Number of columns in block C_i.
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* @param[in] first_row Index of first row of the block C_i (relative to C).
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* @param[in] A Pointer to input matrix A (note: all of it).
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* @param[in] k Number of columns in A and rows in B.
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* @param[in] B Pointer to current column block (panel) of input matrix B.
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* @param[inout] C Pointer to current column block (panel) of output matrix C.
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* @param[in] ldc Offset between elements in adjacent columns in C.
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* @param[in] alpha Scalar factor.
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*/
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static inline void GEBP_block(BLASLONG m, BLASLONG n,
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BLASLONG first_row,
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const FLOAT * restrict A, BLASLONG k,
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const FLOAT * restrict B,
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FLOAT *restrict C, BLASLONG ldc,
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FLOAT alpha)
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{
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A += first_row * k;
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C += first_row;
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#define BLOCK(bm, bn) \
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if (m == bm && n == bn) { \
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GEBP_block_##bm##_##bn(A, k, B, C, ldc, alpha); \
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return; \
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}
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BLOCK(8, 4); BLOCK(8, 2); BLOCK(8, 1);
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BLOCK(4, 4); BLOCK(4, 2); BLOCK(4, 1);
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#ifdef DOUBLE
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BLOCK(2, 4);
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BLOCK(2, 2);
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#endif
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#undef BLOCK
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/* simple implementation for smaller block sizes: */
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FLOAT Caux[m][n] __attribute__ ((aligned (16)));
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/*
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* Peel off first iteration (i.e., column of A) for initializing Caux
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*/
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for (BLASLONG i = 0; i < m; i++)
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for (BLASLONG j = 0; j < n; j++)
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Caux[i][j] = A[i] * B[j];
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for (BLASLONG kk = 1; kk < k; kk++)
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for (BLASLONG i = 0; i < m; i++)
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for (BLASLONG j = 0; j < n; j++)
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Caux[i][j] += A[i + kk * m] * B[j + kk * n];
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for (BLASLONG i = 0; i < m; i++)
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for (BLASLONG j = 0; j < n; j++)
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C[i + j * ldc] += alpha * Caux[i][j];
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}
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/**
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* Handle a column block (panel) of C and B while calculating C += alpha(A * B).
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*
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* @param[in] num_cols Number of columns in the block (in C and B).
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* @param[in] first_col First column of the current block (in C and B).
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* @param[in] A Pointer to input matrix A.
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* @param[in] bk Number of columns in A and rows in B.
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* @param[in] B Pointer to input matrix B (note: all of it).
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* @param[in] bm Number of rows in C and A.
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* @param[inout] C Pointer to output matrix C (note: all of it).
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* @param[in] ldc Offset between elements in adjacent columns in C.
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* @param[in] alpha Scalar factor.
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*/
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static inline void GEBP_column_block(BLASLONG num_cols, BLASLONG first_col,
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const FLOAT *restrict A, BLASLONG bk,
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const FLOAT *restrict B, BLASLONG bm,
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FLOAT *restrict C, BLASLONG ldc,
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FLOAT alpha) {
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FLOAT *restrict C_i = C + first_col * ldc;
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/*
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* B is in column-order with n_r packed row elements, which does
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* not matter -- we always move in full such blocks of
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* column*pack
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*/
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const FLOAT *restrict B_i = B + first_col * bk;
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/*
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* Calculate C_aux := A * B_j
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* then unpack C_i += alpha * C_aux.
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*
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* For that purpose, further partition C_aux and A into blocks
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* of m_r (unroll_m) rows, or powers-of-2 if smaller.
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*/
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BLASLONG row = 0;
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for (BLASLONG block_size = unroll_m; block_size > 0; block_size /= 2)
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for (; bm - row >= block_size; row += block_size)
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GEBP_block(block_size, num_cols, row, A, bk, B_i, C_i,
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ldc, alpha);
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}
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/**
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* Inner kernel for matrix-matrix multiplication. C += alpha (A * B)
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* where C is an m-by-n matrix, A is m-by-k and B is k-by-n. Note that A, B, and
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* C are pointers to submatrices of the actual matrices.
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*
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* @param[in] bm Number of rows in C and A.
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* @param[in] bn Number of columns in C and B.
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* @param[in] bk Number of columns in A and rows in B.
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* @param[in] alpha Scalar factor.
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* @param[in] ba Pointer to input matrix A.
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* @param[in] bb Pointer to input matrix B.
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* @param[inout] C Pointer to output matrix C.
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* @param[in] ldc Offset between elements in adjacent columns in C.
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* @returns 0 on success.
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*/
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int CNAME(BLASLONG bm, BLASLONG bn, BLASLONG bk, FLOAT alpha,
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FLOAT *restrict ba, FLOAT *restrict bb,
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FLOAT *restrict C, BLASLONG ldc)
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{
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if ( (bm == 0) || (bn == 0) || (bk == 0) || (alpha == ZERO))
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return 0;
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/*
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* interface code allocates buffers for ba and bb at page
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* granularity (i.e., using mmap(MAP_ANONYMOUS), so enable the compiler
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* to make use of the fact in vector load operations.
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*/
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ba = __builtin_assume_aligned(ba, 16);
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bb = __builtin_assume_aligned(bb, 16);
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/*
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* Partition B and C into blocks of n_r (unroll_n) columns, called B_i
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* and C_i. For each partition, calculate C_i += alpha * (A * B_j).
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*
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* For remaining columns that do not fill up a block of n_r, iteratively
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* use smaller block sizes of powers of 2.
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*/
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BLASLONG col = 0;
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for (BLASLONG block_size = unroll_n; block_size > 0; block_size /= 2)
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for (; bn - col >= block_size; col += block_size)
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GEBP_column_block(block_size, col, ba, bk, bb, bm, C, ldc, alpha);
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return 0;
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}
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