Skeletons and Transformations in an Integrated Parallel Programming Environment*

Bacci, Bruno; Gorlatch, Sergei; Lengauer, Christian; Pelagatti, Susanna

doi:10.1007/3-540-48387-x_2

Cited by 14 publications

(5 citation statements)

References 8 publications

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“…This is less flexible than having a completely new language but mitigates the drawbacks mentioned. P3L [19] associated with the FAN transformation framework [2,4] is a representative of this approach. While being very efficient, this kind of approach is much more complex to deploy than a Python library and requires a significant effort from the programmer.…”

Section: Related Workmentioning

confidence: 99%

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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Section: Related Workmentioning

confidence: 99%

Automatic Optimization of Python Skeletal Parallel Programs

Loulergue

Philippe

2020

Algorithms and Architectures for Parallel Processing

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“…Skeleton languages: Here we include most of the work done on skeletons. Typical examples are: Bratvold (1993), Darlington et al (1993), Bacci et al (1999), Herrmann (2000), Hamdan (2000) and Michaelson et al (2001). Data-parallel languages: Examples are NESL (Blelloch, 1996), pH (Nikhil et al, 1995), Sisal (Gaudiot et al, 2001) and SaC (Scholz, 1996).…”

Section: The Spectrum Of Parallel Functional Languagesmentioning

confidence: 99%

Parallel functional programming in Eden

2005

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Eden extends the non-strict functional language Haskell with constructs to control parallel evaluation of processes. Although processes are defined explicitly, communication and synchronisation issues are handled in a way transparent to the programmer. In order to offer effective support for parallel evaluation, Eden's coordination constructs override the inherently sequential demand-driven (lazy) evaluation strategy of its computation language Haskell. Eden is a general-purpose parallel functional language suitable for developing sophisticated skeletons – which simplify parallel programming immensely – as well as for exploiting more irregular parallelism that cannot easily be captured by a predefined skeleton. The paper gives a comprehensive description of Eden, its semantics, its skeleton-based programming methodology – which is applied in three case studies – its implementation and performance. Furthermore it points at many additional results that have been achieved in the context of the Eden project.

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“…The most prominent of these approaches are abstract parallel machines (O'Donnell & Rünger, 2000), the TwoL system (Rauber & Rünger, 1996), systems using BSP (Valiant, 1990) as parallel programming model (e.g. Loulergue, 2000), and several systems for deriving skeleton-based parallel code out of Haskell or BMF specifications (Pepper, 1993;Bacci et al, 1999).…”

Section: Derivational Approachesmentioning

confidence: 99%

Parallel and Distributed Haskells

2002

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Parallel and distributed languages specify computations on multiple processors and have a computation language to describe the algorithm, i.e. what to compute, and a coordination language to describe how to organise the computations across the processors. Haskell has been used as the computation language for a wide variety of parallel and distributed languages, and this paper is a comprehensive survey of implemented languages. We outline parallel and distributed language concepts and classify Haskell extensions using them. Similar example programs are used to illustrate and contrast the coordination languages, and the comparison is facilitated by the common computation language. A lazy language is not an obvious choice for parallel or distributed computation, and we address the question of why Haskell is a common functional computation language.

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Skeletons and Transformations in an Integrated Parallel Programming Environment*

Cited by 14 publications

References 8 publications

Automatic Optimization of Python Skeletal Parallel Programs

Automatic Optimization of Python Skeletal Parallel Programs

Parallel functional programming in Eden

Parallel and Distributed Haskells

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