Parallel and Concurrent Programming in Haskell

Chapter 5. Data Parallel Programming with Repa

The techniques we’ve seen in the previous chapters are great for parallelizing code that uses ordinary Haskell data structures like lists and Maps, but they don’t work as well for data-parallel algorithms over large arrays. That’s because large-scale array computations demand very good raw sequential performance, which we can get only by operating on arrays of unboxed data. We can’t use Strategies to parallelize operations over unboxed arrays, because they need lazy data structures (boxed arrays would be suitable, but not unboxed arrays). Similarly, Par doesn’t work well here either, because in Par the data is passed in IVars.

In this chapter, we’re going to see how to write efficient numerical array computations in Haskell and run them in parallel. The library we’re going to use is called Repa, which stands for REgular PArallel arrays.^[19] The library provides a range of efficient operations for creating arrays and operating on arrays in parallel.

The Repa package is available on Hackage. If you followed the instructions for installing the sample code dependencies earlier, then you should already have it, but if not you can install it with cabal install:

$ cabal install repa

In this chapter, I’m going to use GHCi a lot to illustrate the behavior of Repa; trying things out in GHCi is a great way to become familiar with the types and operations that Repa provides. Because Repa provides many operations with the same names as Prelude functions ...