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International audience; PySke is a library of parallel algorithmic skeletons in Python designed for list and tree data structures. Such algorithmic skeletons are high-order functions implemented in parallel. An application developed with PySke is a composition of skeletons. To ease the write of parallel programs, PySke does not follow the Single Program Multiple Data (SPMD) paradigm but offers a global view of parallel programs to users. This approach aims at writing scalable programs easily. In addition to the library, we present experiments performed on a high-performance computing cluster (distributed memory) on a set of example applications developed with PySke.

Explicit parallel programming for shared and distributed memory architectures is an efficient way to deal with data intensive computations. However approaches such as explicit threads or MPI remain difficult solutions for most programmers. Indeed they have to face different constraints such as explicit inter-processors communications or data distribution.

Among distributed systems, connected devices and services, also referred to as the Internet of Things (IoT), are becoming more and more widespread. Some of these devices are used in security-critical domains, and even in domains that are not necessarily critical, privacy issues may arise with devices collecting and transmitting a lot of personal information. It is therefore important to provide security guarantees for the software executed by simple devices, which often do not even provide memory protection units. This kind of guarantees can be brought using formal verification. In this tutorial, we focus on the use of Frama-C, a platform for the analysis of C programs, to verify IoT software. We illustrate it on several examples taken from Contiki, a lightweight operating system for Internet of Things.

Proof assistants such as Coq [1] can be used to develop very high assurance software such as a verified C compiler [2] and verified high performance computing programs [3]. Even when not used for a full system, using such a proof assistant to develop critical parts of a system could be interesting, for example security monitors [4] or the reconfiguration mechanism of a component based system.

International audience; OCaml is a multi-paradigm (functional, imperative,object-oriented) high level sequential language. Types are stati-cally inferred by the compiler and the type system is expressiveand strong. These features make OCaml a very productivelanguage for developing efficient and safe programs. In thistutorial we present three frameworks for using OCaml toprogram scalable parallel architectures: BSML, Multi-ML andSpoc.

With the recent discoveries of widespread vulnerabilities, the use of formal methods in the design of system is more and more important, in particular in the context of Cloud computing which can be used for high performance computing applications [1]. Formal methods range from more lightweight method such as the Alloy [2] formal specification language and analyzer to more heavyweight formal methods based on proof assistants such as Isabelle/HOL or Coq [3].

Structured parallel models such as algorithmic skeletons offer a global view of the parallel program in contrast with the fragmented view of the SPMD style. This makes program easier to write and to read for users, and offer additional opportunities for optimisation done by the libraries, compilers and/or run-time systems. Algorithmic skeletons are or can be seen as patterns or higher-order functions implemented in parallel, often manipulating distributed data structures. Orleans Skeleton Library (OSL) is a library of parallel algorithmic skeletons, written in C++ on top of MPI, which uses meta-programming techniques for optimisation. Often such libraries have no or limited support for arbitrary distributions of the data structures. In this paper we detail the new OSL skeletons used to manage arbitrary distributions of distributed arrays. We present a parallel regular sampling sort application as an example of application that requires such skeletons.

Orleans Skeleton Library (OSL) is a library of parallel algorithmic skeletons in C++ on top of MPI. It provides a structured approach towards parallel programming. Skeletons in OSL are based over the bulk synchronous parallelism model. Applications can be developed using different combinations and compositions of the skeletons. This paper illustrates the expressivity and performance predictability of OSL with two applications: a two dimensional heat diffusion simulation, and an exact N-body simulation. Experiments using these applications are performed on parallel machines.

Bulk Synchronous Parallel ML is a high-level language for programming parallel algorithms. Built upon OCaml and using the BSP model, it provides a safe setting for their implementation, avoiding concurrency related problems (deadlocks, indeterminism). Only a limited set of the features of OCaml can be used in BSML to respect its properties of safety: this paper describes a way to add exception handling to this set by extending and adapting OCaml 's exceptions. After a precise definition of the problems that arise and an informal description of the solutions, an extension of BSML is proposed. Formal semantics define the behaviour in all possible cases, followed by a short description of the implementation.