Predicting Software Defects in Hybrid MPI and OpenMP Parallel Programs Using Machine Learning

High-performance computing (HPC) and its supercomputers are essential for solving the most difficult issues in many scientific computing domains. The proliferation of computational resources utilized by HPC systems has resulted in an increase in the associated error rates. As such, modern HPC system...

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Bibliographic Details
Published in:Electronics (Basel) Vol. 13; no. 1; p. 182
Main Authors: Althiban, Amani S., Alharbi, Hajar M., Al Khuzayem, Lama A., Eassa, Fathy Elbouraey
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.01.2024
Subjects:
Accuracy
Algorithms
Artificial intelligence
C plus plus
Classifiers
Computation
Data mining
Defects
High performance computing
Hybrid systems
Machine learning
Message passing
Methods
Parallel programming
Program errors
Programming environments
Programming languages
Software quality
Support vector machines
System reliability
Saudi Arabia
ISSN:2079-9292, 2079-9292
Online Access:Get full text
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Summary:High-performance computing (HPC) and its supercomputers are essential for solving the most difficult issues in many scientific computing domains. The proliferation of computational resources utilized by HPC systems has resulted in an increase in the associated error rates. As such, modern HPC systems promote a hybrid programming style that integrates the message-passing interface (MPI) and open multi-processing (OpenMP). However, this integration often leads to complex defects, such as deadlocks and race conditions, that are challenging to detect and resolve. This paper presents a novel approach: using machine learning algorithms to predict defects in C++-based systems by employing hybrid MPI and OpenMP models. We focus on employing a balanced dataset to enhance prediction accuracy and reliability. Our study highlights the effectiveness of the support vector machine (SVM) classifier, enhanced with term frequency (TF) and recursive feature elimination (RFE) techniques, which demonstrates superior accuracy and performance in defect prediction when compared to other classifiers. This research contributes significantly to the field by providing a robust method for early defect detection in hybrid programming environments, thereby reducing development time, costs and improving the overall reliability of HPC systems.
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ISSN:2079-9292
2079-9292
DOI:10.3390/electronics13010182