Visible to Intel only — GUID: GUID-A933C893-1AA8-41E5-B60A-B88C380E9430
Package Contents
Parallelizing Simple Loops
Parallelizing Complex Loops
Parallelizing Data Flow and Dependence Graphs
Work Isolation
Exceptions and Cancellation
Containers
Mutual Exclusion
Timing
Memory Allocation
The Task Scheduler
Design Patterns
Migrating from Threading Building Blocks (TBB)
Constrained APIs
Appendix A Costs of Time Slicing
Appendix B Mixing With Other Threading Packages
References
oneapi::tbb::info namespace
parallel_for_each Body semantics and requirements
parallel_sort ranges interface extension
Type-specified message keys for join_node
Scalable Memory Pools
Helper Functions for Expressing Graphs
concurrent_lru_cache
task_arena::constraints extensions
oneapi::tbb::info namespace extensions
task_group extensions
The customizing mutex type for concurrent_hash_map
Visible to Intel only — GUID: GUID-A933C893-1AA8-41E5-B60A-B88C380E9430
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();