Russian Federation
The article presents the development and analysis of a stochastic model of anomalous memory states in multithreaded applications using a vectorized simulator based on Markov chains. The authors classify anomalies, model state transitions and integrate with development infrastructure. The uniqueness of the work lies in detecting hidden anomalies, coverage completeness, 18-fold analysis speedup and scalability to 256+ threads. The article is relevant because in 2026 multithreaded systems suffer from nondeterministic failures leading to outages, while traditional sanitizers miss rare scenarios with probability <0.01%.
memory anomalies, multithreaded applications, data races, stochastic modeling, Markov chains
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