Abstract: In a Parallelizing Compiler, code transformations help to reduce the data dependencies and identify parallelism in a code. In our earlier paper, we proposed a model Data Dependence Identifier (DDI), in which a program P is represented as graph GP . Using 􀀀􀀀 , we could identify data dependencies in a program and also perform transformations like dead code elimination and constant propagation. In this paper, we present algorithms for loop invariant code motion, live range analysis, node splitting and loop fusion code transformations using DDI in polynomial time.

Kavya Alluru and Jeganathan L, “Code Optimizations for Parallelization of Programs using Data Dependence Identifier” International Journal of Advanced Computer Science and Applications(IJACSA), 12(10), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0121093

@article{Alluru2021,
title = {Code Optimizations for Parallelization of Programs using Data Dependence Identifier},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0121093},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0121093},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {10},
author = {Kavya Alluru and Jeganathan L}
}