The module std.parallelism implements high level primitives for convenient concurrent programming.


std.parallelism.parallel allows to automatically distribute a foreach's body to different threads:

// parallel squaring of arr
auto arr = iota(1,100).array;
foreach(ref i; parallel(arr)) {
    i = i*i;

parallel uses the opApply operator internally. The global parallel is a shortcut to taskPool.parallel which is a TaskPool that uses total number of cpus - 1 working threads. Creating your own instance allows to control the degree of parallelism.

Beware that the body of a parallel iteration must make sure that it doesn't modify items that another working unit might have access to.

The optional workingUnitSize specifies the number of elements processed per worker thread.


The function std.algorithm.iteration.reduce - known from other functional contexts as accumulate or foldl - calls a function fun(acc, x) for each element x where acc is the previous result:

// 0 is the "seed"
auto sum = reduce!"a + b"(0, elements);

Taskpool.reduce is the parallel analog to reduce:

// Find the sum of a range in parallel, using the first
// element of each work unit as the seed.
auto sum = taskPool.reduce!"a + b"(nums);

TaskPool.reduce splits the range into sub ranges that are reduced in parallel. Once these results have been calculated, the results are reduced themselves.


task is a wrapper for a function that might take longer or should be executed in its own working thread. It can either be enqueued in a taskpool:

auto t = task!read("foo.txt");

Or directly be executed in its own, new thread:


To get a task's result call yieldForce on it. It will block until the result is available.

auto fileData = t.yieldForce;


rdmd playground.d