PySke: Algorithmic Skeletons for Python

Philippe, Jolan; Loulergue, Frédéric

doi:10.1109/hpcs48598.2019.9188151

Cited by 4 publications

(1 citation statement)

References 36 publications

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“…Basically classes for data structures are used in the abstract data-type style, with a type and its operations, sometimes only non-mutable. This is the approach taken by the PySke library for Python [33] that relies on a rewriting approach for optimization [21]. The patterns used for the design of JFPL, are also mostly absent from many C++ skeleton libraries such as Quaff [8] or OSL [18].…”

Section: Related Workmentioning

confidence: 99%

Reflections on the Design of Parallel Programming Frameworks

Niculescu

Sterca

Loulergue

2021

Communications in Computer and Information Science

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Parallel programming is much more complex and difficult than sequential programming, and it is therefore more challenging to achieve the same software quality in a parallel context. High-level parallel programming models, if implemented as software frameworks, could increase productivity and reliability. Important requirements such as extensibility and adaptability for different platforms are required for such a framework, and this paper reflects on these requirements and their relation to the software engineering methodologies that could put them in practice. All these are exemplified on a Java framework -JPLF; this is a high-level parallel programming approach being based on the model brought by the PowerLists associated theories, and it respects the analysed requirements. The design of JPLF is analysed by explaining the design choices and highlighting the design patterns and design principles applied.

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Section: Related Workmentioning

confidence: 99%

Reflections on the Design of Parallel Programming Frameworks

Niculescu

Sterca

Loulergue

2021

Communications in Computer and Information Science

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Automatic Optimization of Python Skeletal Parallel Programs

Loulergue

Philippe

2020

Algorithms and Architectures for Parallel Processing

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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.

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Towards Automatically Optimizing PySke Programs

Philippe

Loulergue

2019

2019 International Conference on High Performance Computing &Amp; Simulation (HPCS)

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PySke: Algorithmic Skeletons for Python

Cited by 4 publications

References 36 publications

Reflections on the Design of Parallel Programming Frameworks

Reflections on the Design of Parallel Programming Frameworks

Automatic Optimization of Python Skeletal Parallel Programs

Towards Automatically Optimizing PySke Programs

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