A Practical Tree Contraction Algorithm for Parallel Skeletons on Trees of Unbounded Degree

Optimal Tree Contraction and Term Matching on the Hypercube and Related Networks

Algorithmica ◽

10.1007/pl00009165 ◽

1997 ◽

Vol 18 (3) ◽

pp. 445-460 ◽

Cited By ~ 3

Author(s):

E. W. Mayr ◽

R. Werchner

Keyword(s):

Tree Contraction ◽

Optimal Tree

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Hard Optimization Problems in Learning Tree Contraction Patterns

Applied Computing and Information Technology - Studies in Computational Intelligence ◽

10.1007/978-3-319-05717-0_6 ◽

2014 ◽

pp. 77-90

Author(s):

Yasuhiro Okamoto ◽

Takayoshi Shoudai

Keyword(s):

Optimization Problems ◽

Tree Contraction

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Learning Unordered Tree Contraction Patterns in Polynomial Time

Inductive Logic Programming - Lecture Notes in Computer Science ◽

10.1007/978-3-642-38812-5_18 ◽

2013 ◽

pp. 257-272 ◽

Cited By ~ 4

Author(s):

Yuta Yoshimura ◽

Takayoshi Shoudai

Keyword(s):

Polynomial Time ◽

Tree Contraction ◽

Unordered Tree

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Exact analysis of three tree contraction algorithms

Fundamentals of Computation Theory - Lecture Notes in Computer Science ◽

10.1007/3-540-54458-5_81 ◽

1991 ◽

pp. 370-379

Author(s):

Wojciech Plandowski ◽

Wojciech Rytter ◽

Tomasz Szymacha

Keyword(s):

Exact Analysis ◽

Tree Contraction

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Quaff: efficient C++ design for parallel skeletons

Parallel Computing ◽

10.1016/j.parco.2006.06.001 ◽

2006 ◽

Vol 32 (7-8) ◽

pp. 604-615 ◽

Cited By ~ 41

Author(s):

J. Falcou ◽

J. Sérot ◽

T. Chateau ◽

J.T. Lapresté

Keyword(s):

Parallel Skeletons

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AUTO-TUNING PARALLEL SKELETONS

Parallel Processing Letters ◽

10.1142/s0129626412400051 ◽

2012 ◽

Vol 22 (02) ◽

pp. 1240005 ◽

Cited By ~ 2

Author(s):

ALEXANDER COLLINS ◽

CHRISTIAN FENSCH ◽

HUGH LEATHER

Keyword(s):

Execution Time ◽

Human Expert ◽

Programming Abstraction ◽

Parallel Skeletons ◽

Starting Point ◽

Monte Carlo Search ◽

Exploratory Data ◽

Automatic Search ◽

Two Parameters ◽

The Cost

Parallel skeletons are a structured parallel programming abstraction that provide programmers with a predefined set of algorithmic templates that can be combined, nested and parameterized with sequential code to produce complex programs. The implementation of these skeletons is currently a manual process, requiring human expertise to choose suitable implementation parameters that provide good performance. This paper presents an empirical exploration of the optimization space of the FastFlow parallel skeleton framework. We performed this using a Monte Carlo search of a random subset of the space, for a representative set of platforms and programs. The results show that the space is program and platform dependent, non-linear, and that automatic search achieves a significant average speedup in program execution time of 1.6× over a human expert. An exploratory data analysis of the results shows a linear dependence between two of the parameters, and that another two parameters have little effect on performance. These properties are then used to reduce the size of the space by a factor of 6, reducing the cost of the search. This provides a starting point for automatically optimizing parallel skeleton programs without the need for human expertise, and with a large improvement in execution time compared to that achievable using human expert tuning.

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SYSTEMATIC DERIVATION OF TREE CONTRACTION ALGORITHMS

Parallel Processing Letters ◽

10.1142/s0129626405002246 ◽

2005 ◽

Vol 15 (03) ◽

pp. 321-336 ◽

Cited By ~ 5

Author(s):

KIMINORI MATSUZAKI ◽

ZHENJIANG HU ◽

KAZUHIKO KAKEHI ◽

MASATO TAKEICHI

Keyword(s):

Parallel Algorithm ◽

Systematic Method ◽

Recursive Functions ◽

Connected Set ◽

Tree Contraction ◽

Systematic Derivation

While tree contraction algorithms play an important role in efficient tree computation in parallel, it is difficult to develop such algorithms due to the strict conditions imposed on contracting operators. In this paper, we propose a systematic method of deriving efficient tree contraction algorithms from recursive functions on trees. We identify a general recursive form that can be parallelized into efficient tree contraction algorithms, and present a derivation strategy for transforming general recursive functions to the parallelizable form. We illustrate our approach by deriving a novel parallel algorithm for the maximum connected-set sum problem on arbitrary trees, the tree-version of the well-known maximum segment sum problem.

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AN ANALYTICAL METHOD FOR PARALLELIZATION OF RECURSIVE FUNCTIONS

Parallel Processing Letters ◽

10.1142/s0129626400000330 ◽

2000 ◽

Vol 10 (04) ◽

pp. 359-370 ◽

Cited By ~ 1

Author(s):

JOONSEON AHN ◽

TAISOOK HAN

Keyword(s):

Analytical Method ◽

Parallel Programs ◽

Data Parallelism ◽

Complex Data ◽

Recursive Functions ◽

Parallel Skeletons ◽

Data Flows ◽

Recursive Data Structures ◽

Recursive Data

Programming with parallel skeletons is an attractive framework because it encourages programmers to develop efficient and portable parallel programs. However, extracting parallelism from sequential specifications and constructing efficient parallel programs using the skeletons are still difficult tasks. In this paper, we propose an analytical approach to transforming recursive functions on general recursive data structures into compositions of parallel skeletons. Using static slicing, we have defined a classification of subexpressions based on their data-parallelism. Then, skeleton-based parallel programs are generated from the classification. To extend the scope of parallelization, we have adopted more general parallel skeletons which do not require the associativity of argument functions. In this way, our analytical method can parallelize recursive functions with complex data flows.

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