.. _avoiding_data_races:
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.
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