OpenBLAS/kernel/x86_64/sgemm_direct_skylakex.c

479 lines
13 KiB
C

/* the direct sgemm code written by Arjan van der Ven */
#include "common.h"
#if defined(SKYLAKEX) || defined (COOPERLAKE) || defined (SAPPHIRERAPIDS)
#include <immintrin.h>
/*
* "Direct sgemm" code. This code operates directly on the inputs and outputs
* of the sgemm call, avoiding the copies, memory realignments and threading,
* and only supports alpha = 1 and beta = 0.
* This is a common case and provides value for relatively small matrixes.
* For larger matrixes the "regular" sgemm code is superior, there the cost of
* copying/shuffling the B matrix really pays off.
*/
#define DECLARE_RESULT_512(N,M) __m512 result##N##M = _mm512_setzero_ps()
#define BROADCAST_LOAD_A_512(N,M) __m512 Aval##M = _mm512_broadcastss_ps(_mm_load_ss(&A[k + strideA * (i+M)]))
#define LOAD_B_512(N,M) __m512 Bval##N = _mm512_loadu_ps(&B[strideB * k + j + (N*16)])
#define MATMUL_512(N,M) result##N##M = _mm512_fmadd_ps(Aval##M, Bval##N , result##N##M)
#define STORE_512(N,M) _mm512_storeu_ps(&R[(i+M) * strideR + j+(N*16)], result##N##M)
#define DECLARE_RESULT_256(N,M) __m256 result##N##M = _mm256_setzero_ps()
#define BROADCAST_LOAD_A_256(N,M) __m256 Aval##M = _mm256_broadcastss_ps(_mm_load_ss(&A[k + strideA * (i+M)]))
#define LOAD_B_256(N,M) __m256 Bval##N = _mm256_loadu_ps(&B[strideB * k + j + (N*8)])
#define MATMUL_256(N,M) result##N##M = _mm256_fmadd_ps(Aval##M, Bval##N , result##N##M)
#define STORE_256(N,M) _mm256_storeu_ps(&R[(i+M) * strideR + j+(N*8)], result##N##M)
#define DECLARE_RESULT_128(N,M) __m128 result##N##M = _mm_setzero_ps()
#define BROADCAST_LOAD_A_128(N,M) __m128 Aval##M = _mm_broadcastss_ps(_mm_load_ss(&A[k + strideA * (i+M)]))
#define LOAD_B_128(N,M) __m128 Bval##N = _mm_loadu_ps(&B[strideB * k + j + (N*4)])
#define MATMUL_128(N,M) result##N##M = _mm_fmadd_ps(Aval##M, Bval##N , result##N##M)
#define STORE_128(N,M) _mm_storeu_ps(&R[(i+M) * strideR + j+(N*4)], result##N##M)
#define DECLARE_RESULT_SCALAR(N,M) float result##N##M = 0;
#define BROADCAST_LOAD_A_SCALAR(N,M) float Aval##M = A[k + strideA * (i + M)];
#define LOAD_B_SCALAR(N,M) float Bval##N = B[k * strideB + j + N];
#define MATMUL_SCALAR(N,M) result##N##M += Aval##M * Bval##N;
#define STORE_SCALAR(N,M) R[(i+M) * strideR + j + N] = result##N##M;
#if 0
int sgemm_kernel_direct_performant(BLASLONG M, BLASLONG N, BLASLONG K)
{
unsigned long long mnk = M * N * K;
/* large matrixes -> not performant */
if (mnk >= 28 * 512 * 512)
return 0;
/*
* if the B matrix is not a nice multiple if 4 we get many unaligned accesses,
* and the regular sgemm copy/realignment of data pays off much quicker
*/
if ((N & 3) != 0 && (mnk >= 8 * 512 * 512))
return 0;
#ifdef SMP
/* if we can run multithreaded, the threading changes the based threshold */
if (mnk > 2 * 350 * 512 && num_cpu_avail(3)> 1)
return 0;
#endif
return 1;
}
#endif
//void sgemm_kernel_direct (BLASLONG M, BLASLONG N, BLASLONG K, float * __restrict A, BLASLONG strideA, float * __restrict B, BLASLONG strideB , float * __restrict R, BLASLONG strideR)
void CNAME (BLASLONG M, BLASLONG N, BLASLONG K, float * __restrict A, BLASLONG strideA, float * __restrict B, BLASLONG strideB , float * __restrict R, BLASLONG strideR)
{
int i, j, k;
int m4 = M & ~3;
int m2 = M & ~1;
int n64 = N & ~63;
int n32 = N & ~31;
int n16 = N & ~15;
int n8 = N & ~7;
int n4 = N & ~3;
int n2 = N & ~1;
i = 0;
for (i = 0; i < m4; i+=4) {
for (j = 0; j < n64; j+= 64) {
k = 0;
DECLARE_RESULT_512(0, 0); DECLARE_RESULT_512(1, 0); DECLARE_RESULT_512(2, 0); DECLARE_RESULT_512(3, 0);
DECLARE_RESULT_512(0, 1); DECLARE_RESULT_512(1, 1); DECLARE_RESULT_512(2, 1); DECLARE_RESULT_512(3, 1);
DECLARE_RESULT_512(0, 2); DECLARE_RESULT_512(1, 2); DECLARE_RESULT_512(2, 2); DECLARE_RESULT_512(3, 2);
DECLARE_RESULT_512(0, 3); DECLARE_RESULT_512(1, 3); DECLARE_RESULT_512(2, 3); DECLARE_RESULT_512(3, 3);
for (k = 0; k < K; k++) {
BROADCAST_LOAD_A_512(x, 0);
BROADCAST_LOAD_A_512(x, 1);
BROADCAST_LOAD_A_512(x, 2);
BROADCAST_LOAD_A_512(x, 3);
LOAD_B_512(0, x); LOAD_B_512(1, x); LOAD_B_512(2, x); LOAD_B_512(3, x);
MATMUL_512(0, 0); MATMUL_512(1, 0); MATMUL_512(2, 0); MATMUL_512(3, 0);
MATMUL_512(0, 1); MATMUL_512(1, 1); MATMUL_512(2, 1); MATMUL_512(3, 1);
MATMUL_512(0, 2); MATMUL_512(1, 2); MATMUL_512(2, 2); MATMUL_512(3, 2);
MATMUL_512(0, 3); MATMUL_512(1, 3); MATMUL_512(2, 3); MATMUL_512(3, 3);
}
STORE_512(0, 0); STORE_512(1, 0); STORE_512(2, 0); STORE_512(3, 0);
STORE_512(0, 1); STORE_512(1, 1); STORE_512(2, 1); STORE_512(3, 1);
STORE_512(0, 2); STORE_512(1, 2); STORE_512(2, 2); STORE_512(3, 2);
STORE_512(0, 3); STORE_512(1, 3); STORE_512(2, 3); STORE_512(3, 3);
}
for (; j < n32; j+= 32) {
DECLARE_RESULT_512(0, 0); DECLARE_RESULT_512(1, 0);
DECLARE_RESULT_512(0, 1); DECLARE_RESULT_512(1, 1);
DECLARE_RESULT_512(0, 2); DECLARE_RESULT_512(1, 2);
DECLARE_RESULT_512(0, 3); DECLARE_RESULT_512(1, 3);
for (k = 0; k < K; k++) {
BROADCAST_LOAD_A_512(x, 0);
BROADCAST_LOAD_A_512(x, 1);
BROADCAST_LOAD_A_512(x, 2);
BROADCAST_LOAD_A_512(x, 3);
LOAD_B_512(0, x); LOAD_B_512(1, x);
MATMUL_512(0, 0); MATMUL_512(1, 0);
MATMUL_512(0, 1); MATMUL_512(1, 1);
MATMUL_512(0, 2); MATMUL_512(1, 2);
MATMUL_512(0, 3); MATMUL_512(1, 3);
}
STORE_512(0, 0); STORE_512(1, 0);
STORE_512(0, 1); STORE_512(1, 1);
STORE_512(0, 2); STORE_512(1, 2);
STORE_512(0, 3); STORE_512(1, 3);
}
for (; j < n16; j+= 16) {
DECLARE_RESULT_512(0, 0);
DECLARE_RESULT_512(0, 1);
DECLARE_RESULT_512(0, 2);
DECLARE_RESULT_512(0, 3);
for (k = 0; k < K; k++) {
BROADCAST_LOAD_A_512(x, 0);
BROADCAST_LOAD_A_512(x, 1);
BROADCAST_LOAD_A_512(x, 2);
BROADCAST_LOAD_A_512(x, 3);
LOAD_B_512(0, x);
MATMUL_512(0, 0);
MATMUL_512(0, 1);
MATMUL_512(0, 2);
MATMUL_512(0, 3);
}
STORE_512(0, 0);
STORE_512(0, 1);
STORE_512(0, 2);
STORE_512(0, 3);
}
for (; j < n8; j+= 8) {
DECLARE_RESULT_256(0, 0);
DECLARE_RESULT_256(0, 1);
DECLARE_RESULT_256(0, 2);
DECLARE_RESULT_256(0, 3);
for (k = 0; k < K; k++) {
BROADCAST_LOAD_A_256(x, 0);
BROADCAST_LOAD_A_256(x, 1);
BROADCAST_LOAD_A_256(x, 2);
BROADCAST_LOAD_A_256(x, 3);
LOAD_B_256(0, x);
MATMUL_256(0, 0);
MATMUL_256(0, 1);
MATMUL_256(0, 2);
MATMUL_256(0, 3);
}
STORE_256(0, 0);
STORE_256(0, 1);
STORE_256(0, 2);
STORE_256(0, 3);
}
for (; j < n4; j+= 4) {
DECLARE_RESULT_128(0, 0);
DECLARE_RESULT_128(0, 1);
DECLARE_RESULT_128(0, 2);
DECLARE_RESULT_128(0, 3);
for (k = 0; k < K; k++) {
BROADCAST_LOAD_A_128(x, 0);
BROADCAST_LOAD_A_128(x, 1);
BROADCAST_LOAD_A_128(x, 2);
BROADCAST_LOAD_A_128(x, 3);
LOAD_B_128(0, x);
MATMUL_128(0, 0);
MATMUL_128(0, 1);
MATMUL_128(0, 2);
MATMUL_128(0, 3);
}
STORE_128(0, 0);
STORE_128(0, 1);
STORE_128(0, 2);
STORE_128(0, 3);
}
for (; j < n2; j+= 2) {
DECLARE_RESULT_SCALAR(0, 0); DECLARE_RESULT_SCALAR(1, 0);
DECLARE_RESULT_SCALAR(0, 1); DECLARE_RESULT_SCALAR(1, 1);
DECLARE_RESULT_SCALAR(0, 2); DECLARE_RESULT_SCALAR(1, 2);
DECLARE_RESULT_SCALAR(0, 3); DECLARE_RESULT_SCALAR(1, 3);
for (k = 0; k < K; k++) {
BROADCAST_LOAD_A_SCALAR(x, 0);
BROADCAST_LOAD_A_SCALAR(x, 1);
BROADCAST_LOAD_A_SCALAR(x, 2);
BROADCAST_LOAD_A_SCALAR(x, 3);
LOAD_B_SCALAR(0, x); LOAD_B_SCALAR(1, x);
MATMUL_SCALAR(0, 0); MATMUL_SCALAR(1, 0);
MATMUL_SCALAR(0, 1); MATMUL_SCALAR(1, 1);
MATMUL_SCALAR(0, 2); MATMUL_SCALAR(1, 2);
MATMUL_SCALAR(0, 3); MATMUL_SCALAR(1, 3);
}
STORE_SCALAR(0, 0); STORE_SCALAR(1, 0);
STORE_SCALAR(0, 1); STORE_SCALAR(1, 1);
STORE_SCALAR(0, 2); STORE_SCALAR(1, 2);
STORE_SCALAR(0, 3); STORE_SCALAR(1, 3);
}
for (; j < N; j++) {
DECLARE_RESULT_SCALAR(0, 0)
DECLARE_RESULT_SCALAR(0, 1)
DECLARE_RESULT_SCALAR(0, 2)
DECLARE_RESULT_SCALAR(0, 3)
for (k = 0; k < K; k++) {
BROADCAST_LOAD_A_SCALAR(0, 0);
BROADCAST_LOAD_A_SCALAR(0, 1);
BROADCAST_LOAD_A_SCALAR(0, 2);
BROADCAST_LOAD_A_SCALAR(0, 3);
LOAD_B_SCALAR(0, 0);
MATMUL_SCALAR(0, 0);
MATMUL_SCALAR(0, 1);
MATMUL_SCALAR(0, 2);
MATMUL_SCALAR(0, 3);
}
STORE_SCALAR(0, 0);
STORE_SCALAR(0, 1);
STORE_SCALAR(0, 2);
STORE_SCALAR(0, 3);
}
}
for (; i < m2; i+=2) {
j = 0;
for (; j < n64; j+= 64) {
DECLARE_RESULT_512(0, 0); DECLARE_RESULT_512(1, 0); DECLARE_RESULT_512(2, 0); DECLARE_RESULT_512(3, 0);
DECLARE_RESULT_512(0, 1); DECLARE_RESULT_512(1, 1); DECLARE_RESULT_512(2, 1); DECLARE_RESULT_512(3, 1);
for (k = 0; k < K; k++) {
BROADCAST_LOAD_A_512(x, 0);
BROADCAST_LOAD_A_512(x, 1);
LOAD_B_512(0, x); LOAD_B_512(1, x); LOAD_B_512(2, x); LOAD_B_512(3, x);
MATMUL_512(0, 0); MATMUL_512(1, 0); MATMUL_512(2, 0); MATMUL_512(3, 0);
MATMUL_512(0, 1); MATMUL_512(1, 1); MATMUL_512(2, 1); MATMUL_512(3, 1);
}
STORE_512(0, 0); STORE_512(1, 0); STORE_512(2, 0); STORE_512(3, 0);
STORE_512(0, 1); STORE_512(1, 1); STORE_512(2, 1); STORE_512(3, 1);
}
for (; j < n32; j+= 32) {
DECLARE_RESULT_512(0, 0); DECLARE_RESULT_512(1, 0);
DECLARE_RESULT_512(0, 1); DECLARE_RESULT_512(1, 1);
for (k = 0; k < K; k++) {
BROADCAST_LOAD_A_512(x, 0);
BROADCAST_LOAD_A_512(x, 1);
LOAD_B_512(0, x); LOAD_B_512(1, x);
MATMUL_512(0, 0); MATMUL_512(1, 0);
MATMUL_512(0, 1); MATMUL_512(1, 1);
}
STORE_512(0, 0); STORE_512(1, 0);
STORE_512(0, 1); STORE_512(1, 1);
}
for (; j < n16; j+= 16) {
DECLARE_RESULT_512(0, 0);
DECLARE_RESULT_512(0, 1);
for (k = 0; k < K; k++) {
BROADCAST_LOAD_A_512(x, 0);
BROADCAST_LOAD_A_512(x, 1);
LOAD_B_512(0, x);
MATMUL_512(0, 0);
MATMUL_512(0, 1);
}
STORE_512(0, 0);
STORE_512(0, 1);
}
for (; j < n8; j+= 8) {
DECLARE_RESULT_256(0, 0);
DECLARE_RESULT_256(0, 1);
for (k = 0; k < K; k++) {
BROADCAST_LOAD_A_256(x, 0);
BROADCAST_LOAD_A_256(x, 1);
LOAD_B_256(0, x);
MATMUL_256(0, 0);
MATMUL_256(0, 1);
}
STORE_256(0, 0);
STORE_256(0, 1);
}
for (; j < n4; j+= 4) {
DECLARE_RESULT_128(0, 0);
DECLARE_RESULT_128(0, 1);
for (k = 0; k < K; k++) {
BROADCAST_LOAD_A_128(x, 0);
BROADCAST_LOAD_A_128(x, 1);
LOAD_B_128(0, x);
MATMUL_128(0, 0);
MATMUL_128(0, 1);
}
STORE_128(0, 0);
STORE_128(0, 1);
}
for (; j < n2; j+= 2) {
DECLARE_RESULT_SCALAR(0, 0); DECLARE_RESULT_SCALAR(1, 0);
DECLARE_RESULT_SCALAR(0, 1); DECLARE_RESULT_SCALAR(1, 1);
for (k = 0; k < K; k++) {
BROADCAST_LOAD_A_SCALAR(x, 0);
BROADCAST_LOAD_A_SCALAR(x, 1);
LOAD_B_SCALAR(0, x); LOAD_B_SCALAR(1, x);
MATMUL_SCALAR(0, 0); MATMUL_SCALAR(1, 0);
MATMUL_SCALAR(0, 1); MATMUL_SCALAR(1, 1);
}
STORE_SCALAR(0, 0); STORE_SCALAR(1, 0);
STORE_SCALAR(0, 1); STORE_SCALAR(1, 1);
}
for (; j < N; j++) {
DECLARE_RESULT_SCALAR(0, 0);
DECLARE_RESULT_SCALAR(0, 1);
for (k = 0; k < K; k++) {
BROADCAST_LOAD_A_SCALAR(0, 0);
BROADCAST_LOAD_A_SCALAR(0, 1);
LOAD_B_SCALAR(0, 0);
MATMUL_SCALAR(0, 0);
MATMUL_SCALAR(0, 1);
}
STORE_SCALAR(0, 0);
STORE_SCALAR(0, 1);
}
}
for (; i < M; i+=1) {
j = 0;
for (; j < n64; j+= 64) {
DECLARE_RESULT_512(0, 0); DECLARE_RESULT_512(1, 0); DECLARE_RESULT_512(2, 0); DECLARE_RESULT_512(3, 0);
for (k = 0; k < K; k++) {
BROADCAST_LOAD_A_512(x, 0);
LOAD_B_512(0, x); LOAD_B_512(1, x); LOAD_B_512(2, x); LOAD_B_512(3, x);
MATMUL_512(0, 0); MATMUL_512(1, 0); MATMUL_512(2, 0); MATMUL_512(3, 0);
}
STORE_512(0, 0); STORE_512(1, 0); STORE_512(2, 0); STORE_512(3, 0);
}
for (; j < n32; j+= 32) {
DECLARE_RESULT_512(0, 0); DECLARE_RESULT_512(1, 0);
for (k = 0; k < K; k++) {
BROADCAST_LOAD_A_512(x, 0);
LOAD_B_512(0, x); LOAD_B_512(1, x);
MATMUL_512(0, 0); MATMUL_512(1, 0);
}
STORE_512(0, 0); STORE_512(1, 0);
}
for (; j < n16; j+= 16) {
DECLARE_RESULT_512(0, 0);
for (k = 0; k < K; k++) {
BROADCAST_LOAD_A_512(x, 0);
LOAD_B_512(0, x);
MATMUL_512(0, 0);
}
STORE_512(0, 0);
}
for (; j < n8; j+= 8) {
DECLARE_RESULT_256(0, 0);
for (k = 0; k < K; k++) {
BROADCAST_LOAD_A_256(x, 0);
LOAD_B_256(0, x);
MATMUL_256(0, 0);
}
STORE_256(0, 0);
}
for (; j < n4; j+= 4) {
DECLARE_RESULT_128(0, 0);
for (k = 0; k < K; k++) {
BROADCAST_LOAD_A_128(x, 0);
LOAD_B_128(0, x);
MATMUL_128(0, 0);
}
STORE_128(0, 0);
}
for (; j < n2; j+= 2) {
DECLARE_RESULT_SCALAR(0, 0); DECLARE_RESULT_SCALAR(1, 0);
for (k = 0; k < K; k++) {
BROADCAST_LOAD_A_SCALAR(x, 0);
LOAD_B_SCALAR(0, 0); LOAD_B_SCALAR(1, 0);
MATMUL_SCALAR(0, 0); MATMUL_SCALAR(1, 0);
}
STORE_SCALAR(0, 0); STORE_SCALAR(1, 0);
}
for (; j < N; j++) {
DECLARE_RESULT_SCALAR(0, 0);
for (k = 0; k < K; k++) {
BROADCAST_LOAD_A_SCALAR(0, 0);
LOAD_B_SCALAR(0, 0);
MATMUL_SCALAR(0, 0);
}
STORE_SCALAR(0, 0);
}
}
}
#else
void CNAME (BLASLONG M, BLASLONG N, BLASLONG K, float * __restrict A, BLASLONG strideA, float * __restrict B, BLASLONG strideB , float * __restrict R, BLASLONG strideR)
{}
#endif