It has long been assumed in academic circles that functional programming, and declarative processing of streams of immutable data, are convenient and effective tools for parallel programming. Evidence for this is now provided, paradoxically, by the object-imperative Java language, whose version 8 (from 2014) supports functional programming, parallelizable stream processing, and parallel array prefix operations. We illustrate some of these features and use them to solve computational problems that are usually handled by (hard to parallelize) for-loops, and also combinatorial problems such as the n-queens problem, using only streams, higher-order functions and recursion. We show that this declarative approach leads to very good performance on shared-memory multicore machines with a near-trivial parallelization effort on this widely used programming platform. We also highlight a few of the warts caused by the embedding in Java. Some of the examples presented are from Sestoft: Java Precisely, 3rd edition, MIT Press 2016.

Slides:

The slides

The following exercise sheet has been provided by Peter Sestoft. It is not compulsory, but you may find it interesting and educational.

Parallel Functional Programming in Java Exercise Sheet