* know how data is stored in compressed row format like in the SparseMatrix
* class, then a matrix-vector product function looks like this:
* @code
- void SparseMatrix::vmult_one_row (const Vector &src,
- Vector &dst) const
+ void SparseMatrix::vmult (const Vector &src,
+ Vector &dst) const
{
const double *val_ptr = &values[0];
const unsigned int *colnum_ptr = &colnums[0];
* Using the parallel::transform function above, we could in principle write
* this code as follows:
* @code
- void SparseMatrix::vmult (const Vector &src,
- Vector &dst,
- Vector::iterator &dst_row) const
+ void SparseMatrix::vmult_one_row (const Vector &src,
+ Vector &dst,
+ Vector::iterator &dst_row) const
{
const unsigned int row = (dst_row - dst.begin());
* evaluating an error estimator on each cell; or postprocessing on
* each cell computed data for output fall into this class. These
* cases can be treated using a software design pattern we call
- * "%WorkStream". In the following, we will walk through the rationale
+ * WorkStream. In the following, we will walk through the rationale
* for this pattern and its implementation; more details as well as
* examples for the speedup that can be achieved with it are given in
- * the @ref workstream_paper .
+ * the @ref workstream_paper "WorkStream paper".
*
* Code like this could then be written like this:
* @code
*
* In the following, let us lay out a rationale for why the functions in the
* WorkStream namespace are implemented the way they are. More information on
- * their implementation can be found in the @ref workstream_paper .
+ * their implementation can be found in the @ref workstream_paper "WorkStream paper".
* To see the WorkStream class used in practice on tasks like the ones
* outlined above, take a look at the step-9, step-13, step-14, step-32, step-35 or step-37
* tutorial programs.
* is a third version of WorkStream::run that doesn't just take a
* range of iterators, but instead a vector of vectors consisting of
* elements that can be worked on at the same time. This concept
- * is explained in great detail in the @ref workstream_paper , along
- * with performance evaluations for common examples.
+ * is explained in great detail in the @ref workstream_paper "WorkStream paper",
+ * along with performance evaluations for common examples.
*
*
* @anchor MTTaskSynchronization