Abstract
The problem of misallocation of cache lines in processors is studied based on the specific features of memory organization in multi-core and multi-processor systems of distributed systems. Incorrect allocation of cache lines leads to sequential execution of tasks that should be executed in parallel on several cores. It is difficult to determine the problem, and its presence can cause a sharp decrease in the performance of the entire system. In many seismic studies, scientists’ efforts are aimed at finding reliable signs of seismic hazard. Abrupt changes in one or another parameter are called abrupt effects, emissions or anomalies, through which the principle of prediction can be implemented - this means predicting the place, strength and time of a future seismic event. The use of distributed systems in the implementation of predictions with the help of machine learning, digital processing of large volumes of digital signals, ensures fast and high-quality determination of results. To increase the speed, it is necessary to develop parallel algorithms. Synchronization of common parallel streams is necessary to solve parallel computing problems and to ignore cache memory in parallel programming. This causes cache lines to be misallocated. Acceleration of parallel algorithms for digital processing of seismic signals and the dependence of the efficiency of using the system’s computing resources on the number of parallel flows were analyzed, a parallel algorithm for determining the epicenter using cubic splines and a software tool was created based on this algorithm, and the proposed spline method was used to determine the characteristics of P and S waves. Made it possible to determine the detection time 2.5 times faster than usual.
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Mallaev, O., Azimov, B., Muslimjon, K., Kamola, A. (2023). Algorithm for Digital Processing of Seismic Signals in Distributed Systems. In: Zaynidinov, H., Singh, M., Tiwary, U.S., Singh, D. (eds) Intelligent Human Computer Interaction. IHCI 2022. Lecture Notes in Computer Science, vol 13741. Springer, Cham. https://doi.org/10.1007/978-3-031-27199-1_60
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