Intel® oneAPI Threading Building Blocks Developer Guide and API Reference
A newer version of this document is available. Customers should click here to go to the newest version.
Visible to Intel only — GUID: GUID-C5986DE9-6E09-4CC9-BB2E-51B6FC2E6F86
Visible to Intel only — GUID: GUID-C5986DE9-6E09-4CC9-BB2E-51B6FC2E6F86
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* { double *a = read_next_matrix(); 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();