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We present a static analysis for determining the execution costs of lazily evaluated functional languages, such as Haskell. Time-and space-behaviour of lazy functional languages can be hard to predict, creating a significant barrier to their broader acceptance. This paper applies a type-based analysis employing amortisation and cost effects to statically determine upper bounds on evaluation costs. While amortisation performs well with finite recursive data, we significantly improve the precision of our analysis for co-recursive programs (i.e. dealing with potentially infinite data structures) by tracking self-references. Combining these two approaches gives a fully automatic static analysis for both recursive and co-recursive definitions. The analysis is formally proven correct against an operational semantic that features an exchangeable parametric cost-model. An arbitrary measure can be assigned to all syntactic constructs, allowing to bound, for example, evaluation steps, applications, allocations, etc. Moreover, automatic inference only relies on first-order unification and standard linear programming solving. Our publicly available implementation demonstrates the practicability of our technique on editable non-trivial examples.

Abstract. This paper presents a novel type-and-effect analysis for predicting upper-bounds on memory allocation costs for co-recursive definitions in a simple lazily-evaluated functional language. We show the soundness of this system against an instrumented variant of Launchbury's semantics for lazy evaluation which serves as a formal cost model. Our soundness proof requires an intermediate semantics employing indirections. Our proof of correspondence between these semantics that we provide is thus a crucial part of this work. The analysis has been implemented as an automatic inference system. We demonstrate its effectiveness using several example programs that previously could not be automatically analysed.

This paper describes the first successful attempt, of which we are aware, to define an automatic, type-based static analysis of resource bounds for lazy functional programs. Our analysis uses the automatic amortisation approach developed by Hofmann and Jost, which was previously restricted to eager evaluation. In this paper, we extend this work to a lazy setting by capturing the costs of unevaluated expressions in type annotations and by amortising the payment of these costs using a notion of lazy potential. We present our analysis as a proof system for predicting heap allocations of a minimal functional language (including higher-order functions and recursive data types) and define a formal cost model based on Launchbury's natural semantics for lazy evaluation. We prove the soundness of our analysis with respect to the cost model. Our approach is illustrated by a number of representative and non-trivial examples that have been analysed using a prototype implementation of our analysis. The detailed soundness proof and a web version of our analysis are available at http://www.dcc.fc.up.pt/~pbv/cgi/aalazy.cgi. † Note that, because we do not consider deallocation, we model total allocation but not residency.

This paper describes the first successful attempt, of which we are aware, to define an automatic, type-based static analysis of resource bounds for lazy functional programs. Our analysis uses the automatic amortisation approach developed by Hofmann and Jost, which was previously restricted to eager evaluation. In this paper, we extend this work to a lazy setting by capturing the costs of unevaluated expressions in type annotations and by amortising the payment of these costs using a notion of lazy potential. We present our analysis as a proof system for predicting heap allocations of a minimal functional language (including higher-order functions and recursive data types) and define a formal cost model based on Launchbury's natural semantics for lazy evaluation. We prove the soundness of our analysis with respect to the cost model. Our approach is illustrated by a number of representative and non-trivial examples that have been analysed using a prototype implementation of our analysis.

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In this paper an extension of the spectral Lanczos' tau method to systems of nonlinear integro-differential equations is proposed. This extension includes (i) linearization coefficients of orthogonal polynomials products issued from nonlinear terms and (ii) recursive relations to implement matrix inversion whenever a polynomial change of basis is required and (iii) orthogonal polynomial evaluations directly on the orthogonal basis. All these improvements ensure numerical stability and accuracy in the approximate solution. Exposed in detail, this novel approach is able to significantly outperform numerical approximations with other methods as well as different tau implementations. Numerical results on a set of problems illustrate the impact of the mathematical techniques introduced.

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