HPC Based Algorithmic Species Extraction Tool for Automatic Parallelization of Program Code
Mustafa Basthikodi1, Ahmed Rimaz Faizabadi2, Waseem Ahmed3
1Mustafa Basthikodi, Department of CSE, Bearys institute of Technology, Mangalore (Karnataka), India.
2Ahmed Rimaz Faizabadi, Department of CSE, Bearys institute of Technology, Mangalore (Karnataka), India.
3Waseem Ahmed, Department of Computing and IT, King AbdulAziz University, Jeddah, Saudi Arabia.
Manuscript received on 22 July 2019 | Revised Manuscript received on 03 August 2019 | Manuscript Published on 10 August 2019 | PP: 1004-1009 | Volume-8 Issue-2S3 July 2019 | Retrieval Number: B11880782S319/2019©BEIESP | DOI: 10.35940/ijrte.B1188.0782S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Without a doubt, multiple core processors have become primary stream in parallel computing. Therefore, future generations of applications pivotal role will be played by parallelism. It must be noted that, the compilers and programmers could immensely benefit from a program source code classified in a structured manner. Such a classification surely helps programmers to identify parallelization scopes or reasoning about the program code, and associate with other programmers. To address the challenge of parallel programming, we worked on source-to-source compiler Bones and developed species extraction tool extended A-Darwin to ease parallel programming. In the work done, we present ’Algorithmic Species’, a new algorithm classification, that encapsulates required information for parallelization in classes, and embeds memory transfer requirements for optimization of communication on heterogeneous platforms. The evaluation of algorithmic species and the validation of extended A-Darwin are done by testing the tool against the benchmark suit HPCC. The unique approach is developed to generate code automatically for parallel target machines.
Keywords: Program Tool Automatic Classification.
Scope of the Article: Program Understanding and System Maintenance