Conversion from Matlab CSC to CSR format - matlab

Question

I am using mex bridge to perform some operations on Sparse matrices from Matlab.
For that I need to convert input matrix into CSR (compressed row storage) format, since Matlab stores the sparse matrices in CSC (compressed column storage).
I was able to get value array and column_indices array. However, I am struggling to get row_pointer array for CSR format.Is there any C library that can help in conversion from CSC to CSR ?
Further, while writing a CUDA kernel, will it be efficient to use CSR format for sparse operations or should I just use following arrays :- row indices, column indices and values?
Which on would give me more control over the data, minimizing the number for-loops in the custom kernel?

Answer 1

Compressed row storage is similar to compressed column storage, just transposed. So the simplest thing is to use MATLAB to transpose the matrix before you pass it to your MEX file. Then, use the functions
Ap = mxGetJc(spA);
Ai = mxGetIr(spA);
Ax = mxGetPr(spA);
to get the internal pointers and treat them as row storage. Ap is row pointer, Ai is column indices of the non-zero entries, Ax are the non-zero values. Note that for symmetric matrices you do not have to do anything at all! CSC and CSR are the same.
Which format to use heavily depends on what you want to do with the matrix later. For example, have a look at matrix formats for Sparse matrix vector multiplication. That is one of the classic papers, research has moved since then so you can look around further.

Answer 2

I ended up converting CSC format from Matlab to CSR using CUSP library as follows.
After getting the matrix A from matlab and I got its row,col and values vectors and I copied them in respective thrust::host_vector created for each of them.
After that I created two cusp::array1d of type Indices and Values as follows.
typedef typename cusp::array1d<int,cusp::host_memory>Indices;
typedef typename cusp::array1d<float,cusp::host_memory>Values;
Indices row_indices(rows.begin(),rows.end());
Indices col_indices(cols.begin(),cols.end());
Values Vals(Val.begin(),Val.end());
where rows, cols and Val are thrust::host_vector that I got from Matlab.
After that I created a cusp::coo_matrix_view as given below.
typedef cusp::coo_matrix_view<Indices,Indices,Values>HostView;
HostView Ah(m,n,NNZa,row_indices,col_indices,Vals);
where m,n and NNZa are the parameters that I get from mex functions of sparse matrices.
I copied this view matrix to cusp::csr_matrixin device memory with proper dimensions set as given below.
cusp::csr_matrix<int,float,cusp::device_memory>CSR(m,n,NNZa);
CSR = Ah;
After that I just copied the three individual content arrays of this CSR matrix back to the host using thrust::raw_pointer_cast where arrays with proper dimension are already mxCalloced as given below.
cudaMemcpy(Acol,thrust::raw_pointer_cast(&CSR.column_indices[0]),sizeof(int)*(NNZa),cudaMemcpyDeviceToHost);
cudaMemcpy(Aptr,thrust::raw_pointer_cast(&CSR.row_offsets[0]),sizeof(int)*(n+1),cudaMemcpyDeviceToHost);
cudaMemcpy(Aval,thrust::raw_pointer_cast(&CSR.values[0]),sizeof(float)*(NNZa),cudaMemcpyDeviceToHost);
Hope this is useful to anyone who is using CUSP with Matlab

Answer 3

you can do something like this:
n = size(M,1);
nz_num = nnz(M);
[col,rowi,vals] = find(M');
row = zeros(n+1,1);
ll = 1; row(1) = 1;
for l = 2:n
if rowi(l)~=rowi(l-1)
ll = ll + 1;
row(ll) = l;
end
end
row(n+1) = nz_num+1;`
It works for me, hope it can help somebody else!