Intel® oneAPI Threading Building Blocks Developer Guide and API Reference
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Visible to Intel only — GUID: GUID-197849A2-8E1D-4917-94B3-A8A22A0A7458
Avoiding Data Races
The edges in a flow graph make explicit the dependence relationships that you want the library to enforce. Similarly, the concurrency limits on function_node and multifunction_node objects limit the maximum number of concurrent invocations that the runtime library will allow. These are the limits that are enforced by the library; the library does not automatically protect you from data races. You must explicitly prevent data races by using these mechanisms.
For example, the follow code has a data race because there is nothing to prevent concurrent accesses to the global count object referenced by node f:
graph g;
int src_count = 1;
int global_sum = 0;
int limit = 100000;
input_node< int > src( g, [&]( oneapi::tbb::flow_control& fc ) -> int {
if ( src_count <= limit ) {
return src_count++;
} else {
fc.stop();
return int();
}
} );
src.activate();
function_node< int, int > f( g, unlimited, [&]( int i ) -> int {
global_sum += i; // data race on global_sum
return i;
} );
make_edge( src, f );
g.wait_for_all();
cout << "global sum = " << global_sum
<< " and closed form = " << limit*(limit+1)/2 << "\n";
If you run the above example, it will likely calculate a global sum that is a bit smaller than the expected solution due to the data race. The data race could be avoided in this simple example by changing the allowed concurrency in f from unlimited to 1, forcing each value to be processed sequentially by f. You may also note that the input_node also updates a global value, src_count. However, since an input_node always executes serially, there is no race possible.