As one of the developers of the Glasgow Haskell Compiler (GHC) for almost 15 years, I have seen Haskell grow from a niche research language into a rich and thriving ecosystem. I spent a lot of that time working on GHC’s support for parallelism and concurrency. One of the first things I did to GHC in 1997 was to rewrite its runtime system, and a key decision we made at that time was to build concurrency right into the core of the system rather than making it an optional extra or an add-on library. I like to think this decision was founded upon shrewd foresight, but in reality it had as much to do with the fact that we found a way to reduce the overhead of concurrency to near zero (previously it had been on the order of 2%; we’ve always been performance-obsessed). Nevertheless, having concurrency be non-optional meant that it was always a first-class part of the implementation, and I’m sure that this decision was instrumental in bringing about GHC’s solid and lightning-fast concurrency support.
Haskell has a long tradition of being associated with parallelism. To name just a few of the projects, there was the pH variant of Haskell derived from the Id language, which was designed for parallelism, the GUM system for running parallel Haskell programs on multiple machines in a cluster, and the GRiP system: a complete computer architecture designed for running parallel functional programs. All of these happened well before the current multicore revolution, and the problem was that this was the time when Moore’s law was still giving us ever-faster computers. Parallelism was difficult to achieve, and didn’t seem worth the effort when ordinary computers were getting exponentially faster.
Around 2004, we decided to build a parallel implementation of the GHC runtime system for running on shared memory multiprocessors, something that had not been done before. This was just before the multicore revolution. Multiprocessor machines were fairly common, but multicores were still around the corner. Again, I’d like to think the decision to tackle parallelism at this point was enlightened foresight, but it had more to do with the fact that building a shared-memory parallel implementation was an interesting research problem and sounded like fun. Haskell’s purity was essential—it meant that we could avoid some of the overheads of locking in the runtime system and garbage collector, which in turn meant that we could reduce the overhead of using parallelism to a low-single-digit percentage. Nevertheless, it took more research, a rewrite of the scheduler, and a new parallel garbage collector before the implementation was really usable and able to speed up a wide range of programs. The paper I presented at the International Conference on Functional Programming (ICFP) in 2009 marked the turning point from an interesting prototype into a usable tool.
All of this research and implementation was great fun, but good-quality resources for teaching programmers how to use parallelism and concurrency in Haskell were conspicuously absent. Over the last couple of years, I was fortunate to have had the opportunity to teach two summer school courses on parallel and concurrent programming in Haskell: one at the Central European Functional Programming (CEFP) 2011 summer school in Budapest, and the other the CEA/EDF/INRIA 2012 Summer School at Cadarache in the south of