The first abstraction we will look at is the Control.Parallel.Strategies module from the parallel package. The core Strategy API consists of the following:
data Eval a instance Monad Eval type Strategy a = a → Eval a runEval :: Eval a → a using :: a → Strategy a → a rseq :: Strategy a rdeepseq :: NFData a => Strategy a rpar :: Strategy a
The principle is to use using or runEval to evaluate a lazy data structure in parallel, using some strategy. Essentially we have separated the algorithm (a lazy data structure) from the parallel evaluation (a strategy).
As a simple example, consider calculating the minimum and maximum elements of many lists in parallel. We write an algorithm, which doesn't encode any parallelism, called minmax:
-- file: rows.hs import Control.Parallel.Strategies minmax :: [Int] -> (Int, Int) minmax xs = (minimum xs, maximum xs)
Then we have a list of lists (matrix) and a list of minimums and maximums (minmaxes):
matrix = [ [1..1000001],...