The process of writing large parallel programs is complicated by the need to specify both the parallel behaviour of the program and the algorithm that is to be used to compute its result. This paper introduces evaluation strategies: lazy higher-order functions that control the parallel evaluation of non-strict functional languages. Using evaluation strategies, it is possible to achieve a clean separation between algorithmic and behavioural code. The result is enhanced clarity and shorter parallel programs. Evaluation strategies are a very general concept: this paper shows how they can be used to model a wide range of commonly used programming paradigms, including divide-and-conquer parallelism, pipeline parallelism, producer/consumer parallelism, and data-oriented parallelism. Because they are based on unrestricted higher-order functions, they can also capture irregular parallel structures. Evaluation strategies are not just of theoretical interest: they have evolved out of our experience in parallelising several large-scale parallel applications, where they have proved invaluable in helping to manage the complexities of parallel behaviour. Some of these applications are described in detail here. The largest application we have studied to date, Lolita, is a 40,000 line natural language engineering system. Initial results show that for these programs we can achieve acceptable parallel performance, for relatively little programming effort.

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We describe a new automatic static analysis for determining upper-bound functions on the use of quantitative resources for strict, higher-order, polymorphic, recursive programs dealing with possibly-aliased data. Our analysis is a variant of Tarjan's manual amortised cost analysis technique. We use a type-based approach, exploiting linearity to allow inference, and place a new emphasis on the number of references to a data object. The bounds we infer depend on the sizes of the various inputs to a program. They thus expose the impact of specific inputs on the overall cost behaviour.The key novel aspect of our work is that it deals directly with polymorphic higher-order functions without requiring source-level transformations that could alter resource usage. We thus obtain safe and accurate compile-time bounds. Our work is generic in that it deals with a variety of quantitative resources. We illustrate our approach with reference to dynamic memory allocations/deallocations, stack usage, and worst-case execution time, using metrics taken from a real implementation on a simple micro-controller platform that is used in safety-critical automotive applications.

We introduce a reasoning infrastructure for proving statements about resource consumption in a fragment of the Java Virtual Machine Language (JVML). The infrastructure is based on a small hierarchy of program logics, with increasing levels of abstraction: at the top there is a type system for a high-level language that encodes resource consumption. The infrastructure is designed to be used in a proof-carrying code (PCC) scenario, where mobile programs can be equipped with formal evidence that they have predictable resource behaviour.This article focuses on the core logic in our infrastructure, a VDM-style program logic for partial correctness, which can make statements about resource consumption alongside functional behaviour. We establish some important results for this logic, including soundness and completeness with respect to a resource-aware operational semantics for the JVML. We also present a second logic built on top of the core logic, which is used to express termination; it too is shown to be sound and complete. We then outline how high-level language type systems may be connected to these logics.The entire infrastructure has been formalized in Isabelle/HOL, both to enhance the confidence in our meta-theoretical results, and to provide a prototype implementation for PCC. We give examples to show the usefulness of this approach, including proofs of resource bounds on code resulting from compiling high-level functional programs.