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2026-03-27 16:05:53 -06:00
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#include <iostream>
template<typename T, int Rows, int Cols>
struct Matrix {
T data[Rows][Cols];
void setZero() {
for (int i = 0; i < Rows; i++) {
for (int j = 0; j < Cols; j++) {
data[i][j] = T();
}
}
}
};
template<typename T, int M, int N, int P>
Matrix<T, M, P> mat_multiply(Matrix<T, M, N> A, Matrix<T, N, P> B) {
//Determination of resulting matrix size
int RSize;
if(M > P){
RSize = M ;
}
else{
RSize = P;
}
//Condition for resulting matrix size rounding calculation
int CSize;
if (RSize <= 4){
CSize = 4;
}
else if (RSize % 4 == 0){
CSize = RSize;
}
else{
CSize = 4 * (int(RSize/4) + 1);
}
// SIMD width selection (based on your Python logic)
int simd_width;
if (RSize > 8) {
simd_width = 8;
}
else if (RSize > 4 && RSize < 8){
simd_width = 4;
}
else {
simd_width = RSize;
}
// Create result square matrix with size CSize(optimization)
Matrix<T, CSize, CSize> tempResult;
tempResult.setZero();
// Matrix multiplication with SIMD-style unrolling
for (int i = 0; i < M; i++) { // For each row in A
for (int k = 0; k < N; k++) { // For each inner dimension
T a_val = A.data[i][k];
// Process columns in chunks of simd_width
int j = 0;
while (j < P) {
int remaining = P - j;
if (remaining >= simd_width) {
// Full SIMD operation - unrolled loops
if (simd_width == 8) {
tempResult.data[i][j] += a_val * B.data[k][j];
tempResult.data[i][j+1] += a_val * B.data[k][j+1];
tempResult.data[i][j+2] += a_val * B.data[k][j+2];
tempResult.data[i][j+3] += a_val * B.data[k][j+3];
tempResult.data[i][j+4] += a_val * B.data[k][j+4];
tempResult.data[i][j+5] += a_val * B.data[k][j+5];
tempResult.data[i][j+6] += a_val * B.data[k][j+6];
tempResult.data[i][j+7] += a_val * B.data[k][j+7];
}
else if (simd_width == 4) {
tempResult.data[i][j] += a_val * B.data[k][j];
tempResult.data[i][j+1] += a_val * B.data[k][j+1];
tempResult.data[i][j+2] += a_val * B.data[k][j+2];
tempResult.data[i][j+3] += a_val * B.data[k][j+3];
}
else if (simd_width == 3) {
tempResult.data[i][j] += a_val * B.data[k][j];
tempResult.data[i][j+1] += a_val * B.data[k][j+1];
tempResult.data[i][j+2] += a_val * B.data[k][j+2];
}
else if (simd_width == 2) {
tempResult.data[i][j] += a_val * B.data[k][j];
tempResult.data[i][j+1] += a_val * B.data[k][j+1];
}
else { // simd_width == 1
tempResult.data[i][j] += a_val * B.data[k][j];
}
j += simd_width;
}
else {
// Handle remaining columns that don't fit in SIMD width
for (int s = 0; s < remaining; s++) {
tempResult.data[i][j + s] += a_val * B.data[k][j + s];
}
j += remaining;
}
}
}
}
// Extract the actual result (M x P) from the temporary square matrix
Matrix<T, M, P> result;
for (int i = 0; i < M; i++) {
for (int j = 0; j < P; j++) {
result.data[i][j] = tempResult.data[i][j];
}
}
return result;
}