Editing D (programming language) (section)

====Parallelism====
Parallel programming concepts are implemented in the library, and do not require extra support from the compiler. However the D type system and compiler ensure that data sharing can be detected and managed transparently.

<syntaxhighlight lang="D">
import std.stdio : writeln;
import std.range : iota;
import std.parallelism : parallel;

void main() {
    foreach (i; iota(11).parallel) {
        // The body of the foreach loop is executed in parallel for each i
        writeln("processing ", i);
    }
}
</syntaxhighlight>

<code>iota(11).parallel</code> is equivalent to <code>std.parallelism.parallel(iota(11))</code> by using UFCS.

The same module also supports <code>taskPool</code> which can be used for dynamic creation of parallel tasks, as well as map-filter-reduce and fold style operations on ranges (and arrays), which is useful when combined with functional operations. <code>std.algorithm.map</code> returns a lazily evaluated range rather than an array. This way, the elements are computed by each worker task in parallel automatically.

<syntaxhighlight lang="D">
import std.stdio : writeln;
import std.algorithm : map;
import std.range : iota;
import std.parallelism : taskPool;

/* On Intel i7-3930X and gdc 9.3.0:
 * 5140ms using std.algorithm.reduce
 * 888ms using std.parallelism.taskPool.reduce
 *
 * On AMD Threadripper 2950X, and gdc 9.3.0:
 * 2864ms using std.algorithm.reduce
 * 95ms using std.parallelism.taskPool.reduce
 */
void main() {
  auto nums = iota(1.0, 1_000_000_000.0);

  auto x = taskPool.reduce!"a + b"(
      0.0, map!"1.0 / (a * a)"(nums)
  );

  writeln("Sum: ", x);
}
</syntaxhighlight>