cpprandom

This package provides random number generation for Futhark. It is designed to closely imitate <random>, which is very elaborate, but also very flexible. Another complication is due to Futhark's purity, so it is up to the programmer to explicitly manage the RNG state. You are adviced to read the documentation (which contains examples) before using this package. The tests may also be illuminating.

Installation

$ futhark pkg add github.com/diku-dk/cpprandom
$ futhark pkg sync

Usage

The following shows how to combine the random number engine minstd_rand with the distribution module uniform_int_distribution to simulate a single dice roll (throwing away the new RNG state).

$ futhark repl
> import "lib/github.com/diku-dk/cpprandom/random"
> let rng = minstd_rand.rng_from_seed [123]
> module d = uniform_int_distribution i32 minstd_rand
> (d.rand (1,6) rng).1
1

Compatibility

The versioning guarantees for this package do not necessarily involve full replicability. The random sequences may change, even without bumping the major version number, if necessary to fix bugs in seeding or to improve operations such as split_rng.

Performance

The three fastest predefined RNGs are minstd_rand (and its cousin minstd_rand0), xorshift128plus, and pcg32. A simple benchmark program gives the following results on an AMD Vega 64 GPU:

$ futhark bench --backend=opencl benchmark.fut
Compiling benchmark.fut...
Results for benchmark.fut:minstd_rand:
dataset #0 ("128000i32 10000i32"):    5986.30μs (avg. of 10 runs; RSD: 0.06)
Results for benchmark.fut:pcg32:
dataset #0 ("128000i32 10000i32"):    9617.80μs (avg. of 10 runs; RSD: 0.11)
Results for benchmark.fut:xorshift128plus:
dataset #0 ("128000i32 10000i32"):   12447.70μs (avg. of 10 runs; RSD: 0.02)

Note that the randomness produced by minstd_rand is much worse than what is produced by the alternatives.