## Conversion from Matlab CSC to CSR format

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a sparse matrix is a large matrix with almost all elements of the same value

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?

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.

**Conversion from Matlab CSC to CSR format - matlab - iOS,** 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). The CSC format that MATLAB uses stores the data in column order. As I understand it, the CSR format stores the data in row order. You will never get the MATLAB data into a routine (e.g., your CUDA) expecting the data in row order without transposing it.

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_matrix`

in 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 `mxCalloc`

ed 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`

**Can I convert CRS and CSC sparse matrices to MATLAB sparse ,** Learn more about sparse, matrix, convert, load MATLAB. I have data stored in the CRS and CSC formats in MATLAB arrays, and I want to use https://en. wikipedia.org/wiki/Sparse_matrix#Compressed_sparse_row_(CSR� For conversion to the CSR format: If job(6)=0, only arrays ja, ia are filled in for the output storage. If job(6) != 0, all output arrays acsr, ja, and ia are filled in for the output storage. and the same for conversion to the CSC format.

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!

**Create sparse matrix - MATLAB sparse,** If not, how might I convert the format manually and/or is there a better way to call MATLAB stores sparse matrices in the CSC 0-based format, except MATLAB does Convert internal representation back to CSC format (by converting to CSR� CSR format uses three arrays for the above matrix: A simple variation is compressed sparse row format (CSC). format. We can use Matlab’s mex interface to

**How does MATLAB internally store sparse arrays / Calling MKL's ,** One of the operations requires me to convert matrix from CSC to CRS format. I think I will have to use some library that might help in finding the CSR format. MATLAB ® stores sparse matrices in compressed sparse column format. For more information, see John R. Gilbert, Cleve Moler, and Robert Schreiber's Sparse Matrices In MATLAB: Design and Implementation. The accumarray function has similar accumulation behavior to that of sparse.

**Finding CRS format of matrix in Mex - MATLAB Answers,** This practice ensures that the converted sparse matrix has that size. Load the data into MATLAB� and convert it� Converts a sparse matrix in the CSR format to the coordinate format and vice versa. And according to the above link you should set job: if job(1)=1, the matrix in the coordinate format is converted to the CSR format.

**Import from sparse matrix external format,** Converting Full to Sparse. You can convert a full matrix to sparse storage using the sparse function with a single argument. For example:. I want to convert data from SPM tool to .csv format. SPM stores a file in .mat and .dat format. structure is saved in .mat and .dat file contain actual data. Please tell me way to use both of these files and convert to CSV. Also, how I can retrieve the 4 trials in my data.

##### Comments

- This seems like two completely unrelated questions - one to do with sparse matrix format conversion and the other a CUDA programming question. Which is it?
- Second one.Cause I might end up in using triplet format or three arrays for row_indices,col_indices and values.Since I am not able to find a way to get row_ptrs for 'CSR' format....
- What about CUSPARSE? It has conversion routines including the one you are asking about, as well as comprehensive sparse BLAS operations, all without requiring you to write a line of code. And it ships with CUDA, so you already have it....
- @talonmies....Yes I am aware of CUSPARSE and CUSP and I am also aware of the fact that using libraries is always a better option, however, I was wondering whether it will be efficient to write my own kernel for sparse matrix addition/multiplication in CUDA...Thanks for the info about conversion routines in cuSPARSE...there is no direction conversion from CSC to CSR though...I'll have to create a intermediate dense matrix for that part...
- @angainor....Thanks for your suggestion....I was able to convert from CSC to CSR using CUDA CUSP library...
- Perhaps post that as an answer then, @abhinole.