Abstract Over time, several competing approaches to parallel Haskell programming have emerged. Different approaches support parallelism at various different scales, ranging from small multicores to massively parallel high-performance computing systems. They also provide varying degrees of control, ranging from completely implicit approaches to ones providing full programmer control. Most current designs assume a shared memory model at the programmer, implementation and hardware levels. This is, however, becoming increasingly divorced from the reality at the hardware level. It also imposes significant unwanted runtime overheads in the form of garbage collection synchronisation etc. What is needed is an easy way to abstract over the implementation and hardware levels, while presenting a simple parallelism model to the programmer. The PArallEl shAred Nothing runtime system design aims to provide a portable and high-level shared-nothing implementation platform for parallel Haskell dialects. It abstracts over major issues such as work distribution and data serialisation, consolidating existing, successful designs into a single framework. It also provides an optional virtual shared-memory programming abstraction for (possibly) shared-nothing parallel machines, such as modern multicore/manycore architectures or cluster/cloud computing systems. It builds on, unifies and extends, existing well-developed support for shared-memory parallelism that is provided by the widely used GHC Haskell compiler. This paper summarises the state-of-the-art in shared-nothing parallel Haskell implementations, introduces the PArallEl shAred Nothing abstractions, shows how they can be used to implement three distinct parallel Haskell dialects, and demonstrates that good scalability can be obtained on recent parallel machines.

Abstract We compare two inherently different approaches to implement complex process systems in Eden: stable process systems and a compositional approach. A stable process system is characterised by handling several computation stages in each of the participating processes. Often, processes communicate using streams of data, change behaviour with the different computation phases, and more often than not, exactly one process is allocated to each processor element. In contrast, a complex process system can also be achieved by skeleton composition of a number of elementary skeletons, such as parallel transformation, reduction, or special communication patterns. In a compositional implementation, each computation phase leads to a new set of interacting processes. When implementing complex parallel algorithms, skeleton composition is usually easier and more flexible, but has a larger overhead from additional process creation and communication. We present case studies of different parallel application kernels implemented as stable systems and using composition in Eden, including a comprehensive description of Eden's features. Our results show that the compositional performance loss can be alleviated by co-locating processes which directly communicate, and by using Eden's remote data concept to enable such direct communication. Moreover, Eden's parallel runtime system handles communication between co-located processes in an optimised way. EdenTV visualisations of execution traces are invaluable to analyse program characteristics and for targeted optimisations towards better process placement and communication avoidance.

Commodity many-core hardware is now mainstream, but parallel programming models are still lagging behind in efficiently utilizing the application parallelism. There are (at least) two principal reasons for this. First, real-world programs often take the form of a deeply nested composition of parallel operators, but mapping the available parallelism to the hardware requires a set of transformations that are tedious to do by hand and beyond the capability of the common user. Second, the best optimization strategy, such as what to parallelize and what to efficiently sequentialize, is often sensitive to the input dataset and therefore requires multiple code versions that are optimized differently, which also raises maintainability problems. This article presents three array-based applications from the financial domain that are suitable for gpgpu execution. Common benchmark-design practice has been to provide the same code for the sequential and parallel versions that are optimized for only one class of datasets. In comparison, we document (1) all available parallelism via nested map-reduce functional combinators, in a simple Haskell implementation that closely resembles the original code structure, (2) the invariants and code transformations that govern the main trade-offs of a data-sensitive optimization space, and (3) report target cpu and multiversion gpgpu code together with an evaluation that demonstrates optimization trade-offs and other difficulties. We believe that this work provides useful insight into the language constructs and compiler infrastructure capable of expressing and optimizing such applications, and we report in-progress work in this direction.

Domain-specific languages (DSLs) for complex financial contracts are in practical use in many banks and financial institutions today. Given the level of automation and pervasiveness of software in the sector, the financial domain is immensely sensitive to software bugs. At the same time, there is an increasing need to analyse (and report on) the interaction between multiple parties. In this paper, we present a multi-party contract language that rigorously relegates any artefacts of simulation and computation from its core, which leads to favourable algebraic properties, and therefore allows for formalising domain-specific analyses and transformations using a proof assistant. At the centre of our formalisation is a simple denotational semantics independent of any stochastic aspects. Based on this semantics, we devise certified contract analyses and transformations. In particular, we give a type system, with an accompanying type inference procedure, that statically ensures that contracts follow the principle of causality. Moreover, we devise a reduction semantics that allows us to evolve contracts over time, in accordance with the denotational semantics. From the verified Coq definitions, we automatically extract a Haskell implementation of an embedded contract DSL along with the formally verified contract management functionality. This approach opens a road map towards more reliable contract management software, including the possibility of analysing contracts based on symbolic instead of numeric methods.

This paper describes a very high-level approach that aims to orchestrate sequential components written using high-level domain-specific programming into high-performance parallel applications. By achieving this goal, we hope to make parallel programming more accessible to experts in mathematics, engineering and other domains. A key feature of our approach is that parallelism is achieved without any modification to the underlying sequential computational algebra systems, or to the user-level components: rather, all orchestration is performed at an outer level, with sequential components linked through a standard communication protocol, the Symbolic Computing Software Composability Protocol, SCSCP. Despite the generality of our approach, our results show that we are able to achieve very good, and even, in some cases, super-linear, speedups on clusters of commodity workstations: up to a factor of 33.4 on a 28-processor cluster. We are, moreover, able to parallelise a wider variety of problem, and achieve higher performance than typical specialist parallel computational algebra implementations.