I would like to store multiple matmul results as row vector into another matrix, but my current code seems to take a lot of memory space. Here is my pseudo code:

for (int i = 0; i < C_row;   i) {
  C.row(i) = (A.transpose() * B).reshaped(1, C_col);
}

In this case, C is actually a Map of pre-allocated array declared as Map<Matrix<float, -1, -1, RowMajor>> C(C_array, C_row, C_col);. Therefore, I expect the calculated matmul results can directly go to memory space of C and do not create temporary copies. In other words, the total memory usage should be the same with or without the above code. But I found that with the above code, the memory usage is increased significantly.

I tried to use C.row(i).noalias() to directly assign results to each row of C, but there is no memory usage difference. How to make this code more efficiently by taking less memory space?

CodePudding user response：

The reshaped is the culprit. It cannot be folded into the matrix multiplication so it results in a temporary allocation for the multiplication. Ideally you would need to put it onto the left of the assignment:

C.row(i).reshaped(A.cols(), B.cols()).noalias() = A.transpose() * B;

However, that does not compile. Reshaped doesn't seem to fulfil the required interface. It's a pretty new addition to Eigen, so I'm not overly surprised. You might want to open a feature request on their bug tracker.

Anyway, as a workaround, try this:

Eigen::Map<Eigen::MatrixXd> reshaped(C.row(i).data(), A.cols(), B.cols());
reshaped.noalias() = A.transpose() * B;