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/*
* Copyright 2008-2009 NVIDIA Corporation
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef __COO_H__
#define __COO_H__
#include <algorithm>
#include <set>
/*
* Compute B = A for COO matrix A, CSR matrix B
*
*
* Input Arguments:
* I n_row - number of rows in A
* I n_col - number of columns in A
* I nnz - number of nonzeros in A
* I Ai[nnz(A)] - row indices
* I Aj[nnz(A)] - column indices
* T Ax[nnz(A)] - nonzeros
* Output Arguments:
* I Bp - row pointer
* I Bj - column indices
* T Bx - nonzeros
*
* Note:
* Output arrays Bp, Bj, and Bx must be preallocated
*
* Note:
* Input: row and column indices *are not* assumed to be ordered
*
* Note: duplicate entries are carried over to the CSR represention
*
* Complexity: Linear. Specifically O(nnz(A) + max(n_row,n_col))
*
*/
template <class I, class T>
void coo_tocsr(const I n_row,
const I n_col,
const I nnz,
const I Ai[],
const I Aj[],
const T Ax[],
I Bp[],
I Bj[],
T Bx[])
{
//compute number of non-zero entries per row of A
std::fill(Bp, Bp + n_row, 0);
for (I n = 0; n < nnz; n++){
Bp[Ai[n]]++;
}
//cumsum the nnz per row to get Bp[]
for(I i = 0, cumsum = 0; i < n_row; i++){
I temp = Bp[i];
Bp[i] = cumsum;
cumsum += temp;
}
Bp[n_row] = nnz;
//write Aj,Ax into Bj,Bx
for(I n = 0; n < nnz; n++){
I row = Ai[n];
I dest = Bp[row];
Bj[dest] = Aj[n];
Bx[dest] = Ax[n];
Bp[row]++;
}
for(I i = 0, last = 0; i <= n_row; i++){
I temp = Bp[i];
Bp[i] = last;
last = temp;
}
//now Bp,Bj,Bx form a CSR representation (with possible duplicates)
}
template<class I, class T>
void coo_tocsc(const I n_row,
const I n_col,
const I nnz,
const I Ai[],
const I Aj[],
const T Ax[],
I Bp[],
I Bi[],
T Bx[])
{ coo_tocsr<I,T>(n_col, n_row, nnz, Aj, Ai, Ax, Bp, Bi, Bx); }
/*
* Compute B += A for COO matrix A, dense matrix B
*
* Input Arguments:
* I n_row - number of rows in A
* I n_col - number of columns in A
* I nnz - number of nonzeros in A
* I Ai[nnz(A)] - row indices
* I Aj[nnz(A)] - column indices
* T Ax[nnz(A)] - nonzeros
* T Bx[n_row*n_col] - dense matrix
*
*/
template <class I, class T>
void coo_todense(const I n_row,
const I n_col,
const I nnz,
const I Ai[],
const I Aj[],
const T Ax[],
T Bx[])
{
for(I n = 0; n < nnz; n++){
Bx[ n_col * Ai[n] + Aj[n] ] += Ax[n];
}
}
/*
* Compute Y += A*X for COO matrix A and dense vectors X,Y
*
*
* Input Arguments:
* I nnz - number of nonzeros in A
* I Ai[nnz] - row indices
* I Aj[nnz] - column indices
* T Ax[nnz] - nonzero values
* T Xx[n_col] - input vector
*
* Output Arguments:
* T Yx[n_row] - output vector
*
* Notes:
* Output array Yx must be preallocated
*
* Complexity: Linear. Specifically O(nnz(A))
*
*/
template <class I, class T>
void coo_matvec(const I nnz,
const I Ai[],
const I Aj[],
const T Ax[],
const T Xx[],
T Yx[])
{
for(I n = 0; n < nnz; n++){
Yx[Ai[n]] += Ax[n] * Xx[Aj[n]];
}
}
/*
* Count the number of occupied diagonals in COO matrix A
*
* Input Arguments:
* I nnz - number of nonzeros in A
* I Ai[nnz(A)] - row indices
* I Aj[nnz(A)] - column indices
*
*/
template <class I>
I coo_count_diagonals(const I nnz,
const I Ai[],
const I Aj[])
{
std::set<I> diagonals;
for(I n = 0; n < nnz; n++){
diagonals.insert(Aj[n] - Ai[n]);
}
return diagonals.size();
}
#endif
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