FAN:PPA:01 (Article)
|
Author(s) | Marco Aldinucci, Sergei Gorlatch, Christian Lengauer and Susanna Pelagatti |
Title | « Towards Parallel Programming by Transformation: The FAN Skeleton Framework » |
Journal | Parallel Algorithms and Applications |
Volume | 16 |
Number | 2-3 |
Page(s) | 87-121 |
Year | 2001 |
URL | http://calvados.di.unipi.it/storage/paper_files/2001_FAN_paa.pdf |
Abstract |
A Functional Abstract Notation (FAN) is proposed for the specification and design of parallel algorithms by means of skeletons - high-level patterns with parallel semantics. The main weakness of the current programming systems based on skeletons is that the user is still responsible for finding the most appropriate skeleton composition for a given application and a given parallel architecture. We describe a transformational framework for the development of skeletal programs which is aimed at filling this gap. The framework makes use of transformation rules which are semantic equivalences among skeleton compositions. For a given problem, an initial, possibly inefficient skeleton specification is refined by applying a sequence of transformations. Transformations are guided by a set of performance prediction models which forecast the behavior of each skeleton and the performance benefits of different rules. The design process is supported by a graphical tool which locates applicable transformations and provides performance estimates, thereby helping the programmer in navigating through the program refinement space. We give an overview of the FAN framework and exemplify its use with performance-directed program derivations for simple case studies. Our experience can be viewed as a first feasibility study of methods and tools for transformational, performance-directed parallel programming using skeletons. |
@article{FAN:PPA:01,
volume = {16},
number = {2-3},
month = mar,
author = {Marco Aldinucci and Sergei Gorlatch and Christian Lengauer and
Susanna Pelagatti},
url = {http://calvados.di.unipi.it/storage/paper_files/2001_FAN_paa.pdf},
abstract = {A Functional Abstract Notation (FAN) is proposed for the
specification and design of parallel algorithms by means of
skeletons - high-level patterns with parallel semantics. The main
weakness of the current programming systems based on skeletons is
that the user is still responsible for finding the most
appropriate skeleton composition for a given application and a
given parallel architecture. We describe a transformational
framework for the development of skeletal programs which is aimed
at filling this gap. The framework makes use of transformation
rules which are semantic equivalences among skeleton compositions.
For a given problem, an initial, possibly inefficient skeleton
specification is refined by applying a sequence of
transformations. Transformations are guided by a set of
performance prediction models which forecast the behavior of each
skeleton and the performance benefits of different rules. The
design process is supported by a graphical tool which locates
applicable transformations and provides performance estimates,
thereby helping the programmer in navigating through the program
refinement space. We give an overview of the FAN framework and
exemplify its use with performance-directed program derivations
for simple case studies. Our experience can be viewed as a first
feasibility study of methods and tools for transformational,
performance-directed parallel programming using skeletons.},
title = {Towards Parallel Programming by Transformation: The {FAN} Skeleton
Framework},
journal = {Parallel Algorithms and Applications},
pages = {87-121},
year = {2001},
}
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