We describe the design and current status of our effort to implement the programming model of nested data parallelism into the Glasgow Haskell Compiler. We extended the original programmingmodel and its implementation, both of which were first popularised by the NESL language, in terms of expressiveness as well as efficiency. Our current aim is to provide a convenient programming environment for SMP parallelism, and especially multicore architectures. Preliminary benchmarks show that we are, at least for some programs, able to achieve good absolute performance and excellent speedups.

This paper presents an automatic deforestation system, stream fusion, based on equational transformations, that fuses a wider range of functions than existing short-cut fusion systems. In particular, stream fusion is able to fuse zips, left folds and functions over nested lists, including list comprehensions. A distinguishing feature of the framework is its simplicity: by transforming list functions to expose their structure, intermediate values are eliminated by general purpose compiler optimisations. We have reimplemented the Haskell standard List library on top of our framework, providing stream fusion for Haskell lists. By allowing a wider range of functions to fuse, we see an increase in the number of occurrences of fusion in typical Haskell programs. We present benchmarks documenting time and space improvements.

ABSTRACT. 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. 1

Abstract. Haskell is a functional language, with features such as higher order functions and lazy evaluation, which allow succinct programs. These high-level features present many challenges for optimising compilers. We report practical experiments using novel variants of supercompilation, with special attention to let bindings and the generalisation technique. 1

User-defined data types, pattern-matching, and recursion are ubiquitous features of Haskell programs. Sometimes a function is called with arguments that are statically known to already be in constructor form, so that the work of pattern-matching is wasted. Even worse, the argument is sometimes freshly-allocated, only to be immediately decomposed by the function. In this paper we describe a simple, modular transformation that specialises recursive functions according to their argument “shapes”. We show that such a transformation has a simple, modular implementation, and that it can be extremely effective in practice, eliminating both pattern-matching and heap allocation. We describe our implementation of this constructor specialisation transformation in the Glasgow Haskell Compiler, and give measurements of its effectiveness.

Free theorems feature prominently in the field of program transformation for pure functional languages such as Haskell. However, somewhat disappointingly, the semantic properties of so based transformations are often established only very superficially. This paper is intended as a case study showing how to use the existing theoretical foundations and formal methods for improving the situation. To that end, we investigate the correctness issue for a new transformation rule in the short cut fusion family. This destroy/build-rule provides a certain reconciliation between the competing foldr/build- and destroy/unfoldr-approaches to eliminating intermediate lists. Our emphasis is on systematically and rigorously developing the rule’s correctness proof, even while paying attention to semantic aspects like potential nontermination and mixed strict/nonstrict evaluation.

Abstract. For the memory intensive task of graph reduction, modern PCs are limited not by processor speed, but by the rate that data can travel between processor and memory. This limitation is known as the von Neumann bottleneck. We explore the effect of widening this bottleneck using a special-purpose graph reduction machine with wide, parallel memories. Our prototype machine – the Reduceron – is implemented using an FPGA, and is based on a simple template-instantiation evaluator. Running at only 91.5MHz on an FPGA, the Reduceron is faster than mature bytecode implementations of Haskell running on a 2.8GHz PC. 1

We present a novel approach to regular, multi-dimensional arrays in Haskell. The main highlights of our approach are that it (1) is purely functional, (2) supports reuse through shape polymorphism, (3) avoids unnecessary intermediate structures rather than relying on subsequent loop fusion, and (4) supports transparent parallelisation. We show how to embed two forms of shape polymorphism into Haskell’s type system using type classes and type families. In particular, we discuss the generalisation of regular array transformations to arrays of higher rank, and introduce a type-safe specification of array slices. We discuss the runtime performance of our approach for three standard array algorithms. We achieve absolute performance comparable to handwritten C code. At the same time, our implementation scales well up to 8 processor cores. Categories and Subject Descriptors D.3.3 [Programming Languages]: Language Constructs and Features—Concurrent programming structures; Polymorphism; Abstract data types

Abstract. Haskell is a functional language, with features such as higher order functions and lazy evaluation, which allow succinct programs. These high-level features are difficult for fast execution, but GHC is a mature and widely used optimising compiler. This paper presents a wholeprogram approach to optimisation, which produces speed improvements of between 10 % and 60 % when used with GHC, on eight benchmarks. 1

Flattening nested parallelism is a vectorising code transform that converts irregular nested parallelism into flat data parallelism. Although the result has good asymptotic performance, flattening thoroughly restructures the code. Many intermediate data structures and traversals are introduced, which may or may not be eliminated by subsequent optimisation. We present a novel program analysis to identify parts of the program where flattening would only introduce overhead, without appropriate gain. We present empirical evidence that avoiding vectorisation in these cases leads to more efficient programs than if we had applied vectorisation and then relied on array fusion to eliminate intermediates from the resulting code. Categories and Subject Descriptors D.3.3 [Programming Languages]: Language Constructs and Features—Concurrent programming structures; Polymorphism; Abstract data types