Development effort and performance trade-off in high-level parallel programming

Legaux, Joeffrey; Loulergue, Frédéric; Jubertie, Sylvain

doi:10.1109/hpcsim.2014.6903682

Cited by 5 publications

(3 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Halstead metrics [39] and similar metrics are well-designed to evaluate the effort needed to write the same program in different ways. It already have been used by Légaux et al [40], and we plan to use such metrics to provide a comparison of PySke with other parallel programming libraries.…”

Section: Discussionmentioning

confidence: 99%

PySke: Algorithmic Skeletons for Python

Philippe

Loulergue

2019

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

PySke: Algorithmic Skeletons for Python

Philippe

Loulergue

2019

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…PySke is related to SkeTo [13], [14], OSL [15], [16], and Muesli [17], [18], [19]. All these libraries are C++ libraries.…”

Section: Methodsmentioning

confidence: 99%

“…get_partition makes the distribution of the lists visible in the structure itself. For example, get_partition on the list of type PList in Figure 2 yields the PList [[0, 2, 4], [6,8], [10,12], [14,16]] (global view). Now each processor contains only one element (local size 1), but this element is a list.…”

Section: Distribution Changing Skeletonsmentioning

confidence: 99%

New List Skeletons for the Python Skeleton Library

Loulergue

Philippe

2019

2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)

Self Cite

View full text Add to dashboard Cite

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.

show abstract

SyDPaCC: A Framework for the Development of Verified Scalable Parallel Functional Programs

Loulergue,

Ischard

2024

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Development effort and performance trade-off in high-level parallel programming

Cited by 5 publications

References 24 publications

PySke: Algorithmic Skeletons for Python

PySke: Algorithmic Skeletons for Python

New List Skeletons for the Python Skeleton Library

SyDPaCC: A Framework for the Development of Verified Scalable Parallel Functional Programs

Contact Info

Product

Resources

About