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Writing papers is a core research skill for any researcher, but they aren't easy. Writing is not just a way to report on great research; it's a way to do great research. Yet many papers are so badly written that, even if they describe excellent work, the work has much less impact than it should. In this talk I'll give you seven simple, actionable guidelines that will, I hope, help you to write better papers, and have more fun at the same time. I don’t have all the answers—far from it—and I hope that the presentation will evolve into a discussion in which you share your own insights, rather than a lecture.

System FC is an explicitly typed language that serves as the target language for Haskell source programs. System FC is based on System F with the addition of erasable but explicit type equality proof witnesses. Equality proof witnesses are generated from type inference performed on source Haskell programs. Such witnesses may be very large objects, which causes performance degradation in later stages of compilation, and makes it hard to debug the results of type inference and subsequent program transformations. In this paper we present an equality proof simplification algorithm, implemented in GHC, which greatly reduces the size of the target System FC programs.

If you want to program a parallel computer, a purely functional language like Haskell is a promising starting point. Since the language is pure, it is by-default safe for parallel evaluation, whereas imperative languages are by-default unsafe. But that doesn\'t make it easy! Indeed it has proved quite difficult to get robust, scalable performance increases through parallel functional programming, especially as the number of processors increases. A particularly promising and well-studied approach to employing large numbers of processors is data parallelism. Blelloch\'s pioneering work on NESL showed that it was possible to combine a rather flexible programming model (nested data parallelism) with a fast, scalable execution model (flat data parallelism). In this paper we describe Data Parallel Haskell, which embodies nested data parallelism in a modern, general-purpose language, implemented in a state-of-the-art compiler, GHC. We focus particularly on the vectorisation transformation, which transforms nested to flat data parallelism.