A Dynamic Data Dependence Analysis Approach for Software Pipelining

Qiao, Lin; Huang, Weitong; Tang, Zhizhong

doi:10.1007/11577188_29

Cited by 2 publications

(2 citation statements)

References 5 publications

(14 reference statements)

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“…Further experimental results are reported in [12]. On the other hand, a dynamic data dependence analysis approach is presented in [13], which can work with this method together to coping with data dependencies for software pipelining.…”

Section: Resultsmentioning

confidence: 99%

A Static Data Dependence Analysis Approach for Software Pipelining

Qiao

Huang

Tang

2005

Lecture Notes in Computer Science

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This paper introduces a new static data dependence constraint, called dependence difference inequality, which can deal with coupled subscripts for multi-dimensional array references. Unlike direction vectors, dependence difference inequalities are related to not only the iteration space for a loop program but also the operation distance between two operations. They are more strict than other methods, and can act as additional constraints to each variable in a linear system on their own or with others. As a result, the solution space for a linear system can be compressed heavily. So long as dependence difference inequalities do not satisfy simultaneously, the loop can be software-pipelined with any initiation interval even if there exists a data dependence between two operations. Meanwhile, by replacing direction vectors with dependence difference inequalities some conservative estimations made by other traditional data dependence analysis approaches can be eliminated.

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Section: Resultsmentioning

confidence: 99%

A Static Data Dependence Analysis Approach for Software Pipelining

Qiao

Huang

Tang

2005

Lecture Notes in Computer Science

Self Cite

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show abstract

“…On the other hand, a dynamic data dependence analysis approach is presented in [7], which can work with this method together to coping with data dependencies for software pipelining.…”

Section: Resultsmentioning

confidence: 99%

Coping with Data Dependencies of Multi-dimensional Array References

Qiao

Huang

Tang

2005

Lecture Notes in Computer Science

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This paper presents a new static data dependence analysis approach, Dependence Difference Inequality Test, which can deal with coupled subscripts for multi-dimensional array references for software pipelining techniques for nested loops. The Dependence Difference Inequality Test (DDIT) replaces direction vectors with dependence difference inequalities as constraints to variables in a linear system. The method presented in this paper extends the applicable range of the Generalized Lambda Test and seems to be a practical scheme to analyze data dependence. Experimental results show that the number of data independences checked by the DDIT algorithm is slightly smaller than that manually. It is also shown that our method is better than other traditional data dependence analysis methods without increasing time cost: it increases the success rate of the Generalized Lambda Test by approximately 14.19%.

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A Dynamic Data Dependence Analysis Approach for Software Pipelining

Cited by 2 publications

References 5 publications

A Static Data Dependence Analysis Approach for Software Pipelining

A Static Data Dependence Analysis Approach for Software Pipelining

Coping with Data Dependencies of Multi-dimensional Array References

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