Abstract
PowerList theory is well suited to express recursive, data-parallel algorithms. Its abstractness is very high and ensures simple and correct design of parallel programs. We try to reconcile this high level of abstraction with performance by introducing data-distributions into this theory. One advantage of formally introducing distributions is that it allows us to evaluate costs, depending on the number of available processors, which is considered as a parameter. The analysis of the possible distributions for a certain function may also lead to an improvement in the design decisions. Another important advantage is that after the introduction of data-distributions, mappings on real parallel architectures with limited number of processing elements can be analyzed. Case studies for Fast Fourier transform and rank-sorting are given.
This is a preview of subscription content, log in via an institution.
Preview
Unable to display preview. Download preview PDF.
References
Bird, R.: Lectures on Constructive Functional Programming. In: Broy, M. (ed.) Constructive Methods in Computing Science. NATO ASI Series F: Computer and Systems Sciences, vol. 55, pp. 151–216. Springer, Heidelberg (1988)
Cole, M.: Parallel Programming with List Homomorphisms. Parallel Processing Letters 5(2), 191–204 (1994)
Gorlatch, S.: Abstraction and Performance in the Design of Parallel Programs. In: CMPP 1998 First International Workshop on Constructive Methods for Parallel Programming (1998)
Jay, C.B.: A semantics for shape. Science of Computer Programming 25(2), 251–283(33) (1995)
Jay, C.B.: Costing Parallel Programs as a Function of Shapes. Science of Computer Programming 37(1), 207–224 (2000)
Knuth, D.E.: The Art of Computer Programming. In: Sorting and Searching, vol. 3, Addison-Wesley, Reading (1973)
Kornerup, J.: Data Structures for Parallel Recursion. PhD Thesis, Univ. of Texas (1997)
Kornerup, J.: PLists: Taking PowerLists Beyond Base Two. In: MIP-9805, 9805th edn. First International Workshop on Constructive Methods for Parallel Programming, pp. 102–116 (1998)
Misra, J.: PowerList: A structure for parallel recursion. ACM Transactions on Programming Languages and Systems 16(6), 1737–1767 (1994)
Niculescu, V.: On Data Distributions in the Construction of Parallel Programs. The Journal of Supercomputing 29(1), 5–25 (2004)
Niculescu, V.: Unbounded and Bounded Parallelism in BMF. Case-Study: Rank Sorting. Studia Universitatis Babes-Bolyai, Informatica XLIX(1), 91–98 (2004)
Skillicorn, D.B.: Structuring data parallelism using categorical data types. In: Programming Models for Massively Parallel Computers, pp. 110–115. Computer Society Press (1993)
Skillicorn, D.B., Talia, D.: Models and Languages for Parallel Computation. ACM Computer surveys 30(2), 123–136 (1998)
Wilkinson, B., Allen, M.: Parallel Programming Techniques and Applications Using Networked Workstations and Parallel Computers. Prentice-Hall, Englewood Cliffs (2002)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Niculescu, V. (2007). Data-Distributions in PowerList Theory. In: Jones, C.B., Liu, Z., Woodcock, J. (eds) Theoretical Aspects of Computing – ICTAC 2007. ICTAC 2007. Lecture Notes in Computer Science, vol 4711. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75292-9_27
Download citation
DOI: https://doi.org/10.1007/978-3-540-75292-9_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75290-5
Online ISBN: 978-3-540-75292-9
eBook Packages: Computer ScienceComputer Science (R0)