Extensive Evaluation of Programming Models and ISAs Impact on Multicore So Error Reliability

Rosa, Felipe; Bandeira, Vitor; Reis, Ricardo; Ost, Luciano

doi:10.1109/dac.2018.8465855

Cited by 2 publications

(1 citation statement)

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“…While parallel programs can run much faster than sequential programs, they are also more likely to contain errors because they are more complex. Rosa et al [15] highlighted that parallel programs are more difficult to write and debug than sequential programs because they must co-ordinate the execution of multiple threads on multiple processors. This can lead to several errors, such as race conditions and deadlocks.…”

Section: Introductionmentioning

confidence: 99%

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

Althiban,

Alharbi,

Al Khuzayem

et al. 2023

Electronics

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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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Section: Introductionmentioning

confidence: 99%