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Bulk-Synchronous Parallel ML (BSML) is a ML based language to code Bulk-Synchronous Parallel (BSP) algorithms. It allows an estimation of execution time, avoids deadlocks and non-determinism. BSML proposes an extension of ML programming with parallel primitives on a parallel data structure called parallel vector. One of these primitives is dedicated to… (More)

- Roberto M. Amadio, Nicholas Ayache, François Bobot, Jaap Boender, Brian Campbell, Ilias Garnier +9 others
- FOPARA
- 2013

We provide an overview of the FET-Open Project CerCo ('Certified Complexity'). Our main achievement is the development of a technique for analysing non-functional properties of programs (time, space) at the source level with little or no loss of accuracy and a small trusted code base. The core component is a C compiler, verified in Matita, that produces an… (More)

BSML is a ML based language designed to code Bulk Synchronous Parallel (BSP) algorithms. It allows an estimation of execution time, avoids deadlocks and non-determinism. BSML proposes an extension of ML programming with a small set of primitives. One of these primitives, called parallel superposition, allows the parallel composition of two BSP programs.… (More)

BSML is an ML based language designed to code Bulk Synchronous Parallel (BSP) algorithms. It allows an estimation of execution time, avoids deadlocks and non-determinism. BSML proposes an extension of ML programming with a small set of primitives. One of these primitives, called parallel superposition, allows the parallel composition of two BSP programs.… (More)

This paper presents an investigation of the notion of reaction time in some synchronous systems. A state-based description of such systems is given, and the reaction time of such systems under some classic composition primitives is studied. Reaction time is shown to be non-compositional in general. Possible solutions are proposed, and applications to… (More)

The process of inverting Markov kernels relates to the important subject of Bayesian modelling and learning. In fact, Bayesian update is exactly kernel inversion. In this paper, we investigate how and when Markov kernels (aka stochastic relations, or probabilistic mappings, or simply kernels) can be inverted. We address the question both directly on the… (More)

We propose an algebraic approach to stochastic graph-rewriting which extends the classical construction of the Heisenberg-Weyl algebra and its canonical representation on the Fock space. Rules are seen as particular elements of an algebra of "diagrams": the diagram algebra D. Diagrams can be thought of as formal computational traces represented in partial… (More)

We present a method for constructing robustly parameterised families of higher-order probab-ilistic models. Parameter spaces and models are represented by certain classes of functors in the category of Polish spaces. Maps from parameter spaces to models (parameterisations) are continuous and natural transformations between such functors. Naturality ensures… (More)