# Use Nested Algorithms to Increase Scalability#

One powerful way to increase the scalability of a flow graph is to nest other parallel algorithms inside of node bodies. Doing so, you can use a flow graph as a coordination language, expressing the most coarse-grained parallelism at the level of the graph, with finer grained parallelism nested within.

In the example below, five nodes are created: an `input_node`, `matrix_source`, that reads a sequence of matrices from a file, two `function_nodes`, `n1` and `n2`, that receive these matrices and generate two new matrices by applying a function to each element, and two final `function_nodes`, `n1_sink` and `n2_sink`, that process these resulting matrices. The `matrix_source` is connected to both `n1` and `n2`. The node `n1` is connected to `n1_sink`, and `n2` is connected to `n2_sink`. In the lambda expressions for `n1` and `n2`, a `parallel_for` is used to apply the functions to the elements of the matrix in parallel. The functions `read_next_matrix`, `f1`, `f2`, `consume_f1` and `consume_f2` are not provided below.

```graph g;
input_node< double * > matrix_source( g, [&]( oneapi::tbb::flow_control &fc ) -> double* {
if ( a ) {
return a;
} else {
fc.stop();
return nullptr;
}
} );
function_node< double *, double * > n1( g, unlimited, [&]( double *a ) -> double * {
double *b = new double[N];
parallel_for( 0, N, [&](int i) {
b[i] = f1(a[i]);
} );
return b;
} );
function_node< double *, double * > n2( g, unlimited, [&]( double *a ) -> double * {
double *b = new double[N];
parallel_for( 0, N, [&](int i) {
b[i] = f2(a[i]);
} );
return b;
} );
function_node< double *, double * > n1_sink( g, unlimited,
[]( double *b ) -> double * {
return consume_f1(b);
} );
function_node< double *, double * > n2_sink( g, unlimited,
[]( double *b ) -> double * {
return consume_f2(b);
} );
make_edge( matrix_source, n1 );
make_edge( matrix_source, n2 );
make_edge( n1, n1_sink );
make_edge( n2, n2_sink );
matrix_source.activate();
g.wait_for_all();
```