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Erschienen in: The Journal of Supercomputing 4/2018

15.11.2017

K-DT: a formal system for the evaluation of linear data dependence testing techniques

verfasst von: Jie Zhao, Rongcai Zhao

Erschienen in: The Journal of Supercomputing | Ausgabe 4/2018

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Abstract

The power of data dependence testing techniques of a parallelizing compiler is its essence to transform and optimize programs. Numerous techniques were proposed in the past, and it is, however, still a challenging problem to evaluate the relative power of these techniques to better understand the data dependence testing problem. In the past, either empirical studies or experimental evaluation results are published to compare these data dependence testing techniques, being not able to convince the research community completely. In this paper, we show a theoretical study on this issue, comparing the power on disproving dependences of existing techniques by proving theorems in a proposed formal system K-DT. Besides, we also present the upper bounds of these techniques and introduce their minimum complete sets. To the best of our knowledge, K-DT is the first formal system used to compare the power of data dependence testing techniques, and this paper is the first work to show the upper bounds and minimum complete sets of data dependence testing techniques.

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Fußnoten
1
Linpack, Eispack and the Perfect Benchmarks that will be introduced later, are different sets of benchmarks targeting performance evaluation of parallel programs generated by parallelizing compilers.
 
2
It seems like \(GCD(e)\rightarrow \sim Solvable(e,UI)\) mismatches Axiom (D7). However, we conclude in Table 1 that \(GCD(e)\leftrightarrow \sim Solvable(e,UI)\), so we write them in this form to make the deductions concise. So are the situations in Theorems 7, 9 and 10.
 
3
A real shadow is the whole shadow cast by an object while a dark shadow is clearly dark below any part of the object that is at least one unit thick. Refer to [24] for more details.
 
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Metadaten
Titel
K-DT: a formal system for the evaluation of linear data dependence testing techniques
verfasst von
Jie Zhao
Rongcai Zhao
Publikationsdatum
15.11.2017
Verlag
Springer US
Erschienen in
The Journal of Supercomputing / Ausgabe 4/2018
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-017-2187-3

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