Automatic code parallelization for data-intensive computing in multicore systems

A major driving force behind the increasing popularity of data science is the increasing need for data-driven analytics fuelled by massive amounts of complex data. Increasingly, parallel processing has become a cost-effective method for computationally large and data-intensive problems. Many existin...

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Bibliographic Details
Published in:Journal of physics. Conference series Vol. 1411; no. 1; pp. 12014 - 12022
Main Authors: Subramanian, Ranjini, Zhang, Hui
Format: Journal Article
Language:English
Published: Bristol IOP Publishing 01.11.2019
Subjects:
Microprocessors
Parallel processing
Parallel programming
Physics
ISSN:1742-6588, 1742-6596
Online Access:Get full text
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Summary:A major driving force behind the increasing popularity of data science is the increasing need for data-driven analytics fuelled by massive amounts of complex data. Increasingly, parallel processing has become a cost-effective method for computationally large and data-intensive problems. Many existing applications are sequential in nature and if such applications are ported to multi-processor systems for execution, they would make use of only one core and the optimal usage of all cores is not guaranteed. Knowledge of parallel programming is necessary to ensure the use of processing power offered by multi-processor systems in order to achieve better performance. However, many users do not possess the skills and knowledge required to convert existing sequential code to parallel code to achieve speedups and scalability. In this paper, we introduce a framework that automatically transforms existing sequential code to parallel code while ensuring functional correctness using divide-and-conquer paradigm, so that the benefits offered by multi-core systems can be maximized. The paper will outline the implementation of the framework and demonstrate its usage with practical use cases.
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ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1411/1/012014