Abstract
The paper proposes a metric that evaluates the overhead introduced into parallel programs by the additional operations that parallelism implicitly imposes. We consider the case of multithreaded parallel programs that follows SPMD (Single Program Multiple Data) model. Java programs were considered for this proposal, but the metric could be easily adapted for any multithreading supporting imperative language. The metric is defined as a combination of several atomic metrics considering various synchronisation mechanisms that could be discovered using the source code analysis. A theoretical validation of this metric is presented, and an empirical evaluation of several use cases. Additionally, we propose an Artificial Intelligence based strategy to refine the evaluation of the metric by obtaining approximation for the weights that are used in combining the considered atomic metrics. The methodology of the approach is statistical using the multiple linear regression, which considers as dependent variable the execution times of different concrete use-cases, and as independent variables the corresponding overhead times introduced by the considered synchronization mechanisms, which are approximated through the atomic metrics. The results indicated a high degree of correlation between the dependent and independent variables. The Root Mean Square Error obtained is 0.155186, thus being very small, the predicted and observed values are very close.
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The complete code could be accessed at the following URL: “shorturl.at/bjlmT”.
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Niculescu, V., Şerban, C., Vescan, A. (2023). A New Metric for Multithreaded Parallel Programs Overhead Time Prediction. In: Kaindl, H., Mannion, M., Maciaszek, L.A. (eds) Evaluation of Novel Approaches to Software Engineering. ENASE 2022. Communications in Computer and Information Science, vol 1829. Springer, Cham. https://doi.org/10.1007/978-3-031-36597-3_15
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