Regent is a language for implicit dataflow parallelism.

Regent discovers dataflow parallelism in sequential code by computing a dependence graph over tasks, like the one below. Tasks execute as soon as all dependencies are satisfied, and can be distributed automatically over a cluster of (possibly heterogeneous) machines.

Because execution follows the original sequential semantics of the code, Regent programs are easy to read and understand. Just read the code top-to-bottom, as if it were written in a traditional sequential language. The dependence graph above is produced when the task main executes below.

import "regent"

struct point { x : float, y : float } – A simple struct with two fields.


– Define 4 tasks. Ignore the task bodies for the moment; the behavior of each
– task is soundly described by its declaration. Note that each declaration
– says what the task will read or write.
task a(points : region(point)) where writes(points) do –[[ … ]] end
task b(points : region(point)) where reads writes(points.x) do –[[ … ]] end
task c(points : region(point)) where reads writes(points.y) do –[[ … ]] end
task d(points : region(point)) where reads(points) do –[[ … ]] end


– Execution begins at main. Read the code top-down (like a sequential program).
task main()
– Create a region (like an array) with room for 5 elements.
var points = region(ispace(ptr, 5), point)
new(ptr(point, points), 5) – Allocate the elements.


– Partition the region into 3 subregions. Each subregion is a view onto a
– subset of the data of the parent.
var part = partition(equal, points, ispace(int1d, 3))


– Launch subtasks a, b, c, and d.
a(points)
for i = 0, 3 do
b(part[i])
end
c(points)
for i = 0, 3 do
d(part[i])
end
end
regentlib.start(main)

Interested in learning more? Install Regent and checkout the tutorials.