Low-code development platforms are taking an important place in the model-driven engineering ecosystem, raising new challenges, among which transparent e ciency or scalability. Indeed, the increasing size of models leads to di culties in interacting with them e ciently. To tackle this scalability issue, some tools are built upon speci c computational strategies exploiting reactivity, or parallelism. However, their performances may vary depending on the speci c nature of their usage. Choosing the most suitable computational strategy for a given usage is a di cult task which should be automated. Besides, the most e cient solutions may be obtained by the use of several strategies at the same time. is paper motivates the need for a transparent multi-strategy execution mode for model-management operations. We present an overview of the di erent computational strategies used in the model-driven engineering ecosystem, and use a running example to introduce the bene ts of mixing strategies for performing a single computation. is example helps us present our design ideas for a multi-strategy model-management system. e code-related and DevOps challenges that emerged from this analysis are also presented. CCS CONCEPTS •So ware and its engineering →So ware creation and management; •Computing methodologies →Parallel algorithms;

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 highperformance computing cluster (distributed memory) on a set of example applications developed with PySke.

Skeletal parallelism is a model of parallelism where parallel constructs are provided to the programmer as usual patterns of parallel algorithms. Highlevel skeleton libraries often offer a global view of programs instead of the common Single Program Multiple Data view in parallel programming. A program is written as a sequential program but operates on parallel data structures. Most of the time, skeletons on a parallel data structure have counterparts on a sequential data structure. For example, the map function that applies a given function to all the elements of a sequential collection (e.g., a list) has a map skeleton counterpart that applies a sequential function to all the elements of a distributed collection. Two of the challenges a programmer faces when using a skeleton library that provides a wide variety of skeletons are: which are the skeletons to use, and how to compose them? These design decisions may have a large impact on the performance of the parallel programs. However, skeletons, especially when they do not mutate the data structure they operate on, but are rather implemented as pure functions, possess algebraic properties that allow to transform compositions of skeletons into more efficient compositions of skeletons. In this paper, we present such an automatic transformation framework for the Python skeleton library PySke and evaluate it on several example applications.

Algorithmic skeletons are patterns of parallel computations. Skeletal parallel programming eases parallel programming: a program is merely a composition of such patterns. Dataparallel skeletons operate on parallel data-structures that have often sequential counterparts. In algorithmic skeleton approaches that offer a global view of programs, a parallel program has therefore a structure similar to a sequential program but operates on parallel data-structures. PySke is such an algorithmic skeleton library for Python to program shared or distributed memory parallel architectures in a simple way. This paper presents an extension to PySke: new algorithmic skeletons on parallel lists. This extension is evaluated on an application.Index Terms-high-level parallel programming, structured parallelism, algorithmic skeleton, Python, lists.