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The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.

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Research Article | Open Access |

Code Optimizations for Parallelization of Programs using Data Dependence Identifier

Author 1: Kavya Alluru Author 2: Jeganathan L
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 12, No. 10 · Published 2021

DOI: https://doi.org/10.14569/IJACSA.2021.0121093

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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.

Keywords

Automatic parallelization parallelizing compilers code optimizations data dependence loop invariant code motion node splitting live range analysis loop fusion

How to Cite this Article

Alluru, K., & L, J. (2021). Code Optimizations for Parallelization of Programs using Data Dependence Identifier. International Journal of Advanced Computer Science and Applications, 12(10). https://doi.org/10.14569/IJACSA.2021.0121093

Alluru, Kavya, and Jeganathan L. "Code Optimizations for Parallelization of Programs using Data Dependence Identifier." International Journal of Advanced Computer Science and Applications, vol. 12, no. 10, 2021, https://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},
  volume    = {12},
  number    = {10},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Kavya Alluru and Jeganathan L},
  doi       = {10.14569/IJACSA.2021.0121093},
  url       = {https://doi.org/10.14569/IJACSA.2021.0121093}
}

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