Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/32232
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dc.contributor.authorVelichkovski, Gordonen_US
dc.contributor.authorGusev, Marjanen_US
dc.contributor.authorMileski, Dimitaren_US
dc.date.accessioned2025-01-09T10:18:04Z-
dc.date.available2025-01-09T10:18:04Z-
dc.date.issued2024-11-26-
dc.identifier.urihttp://hdl.handle.net/20.500.12188/32232-
dc.description.abstractEntropy algorithms are crucial in fields where assessing randomness, uncertainty, or complexity is vital. As datasets grow, efficient entropy calculations become important. This work explores the parallelization of Shannon entropy calculations, using GPU acceleration through CUDA for sliding window systems. By leveraging GPUs’ parallel architecture, the approach achieves up to 15x speedup for large datasets. However, smaller datasets show limited improvements due to overhead, underscoring the need for optimization to harness GPU acceleration’s potential.en_US
dc.publisherIEEEen_US
dc.subjectUncertainty , Graphics processing units , Entropy , Complexity theory , Telecommunications , Parallel architectures , Optimizationen_US
dc.titleCUDA Calculation of Shannon Entropy for a Sliding Window Systemen_US
dc.typeArticleen_US
dc.relation.conference2024 32nd Telecommunications Forum (TELFOR)en_US
dc.identifier.doi10.1109/telfor63250.2024.10819103-
dc.identifier.urlhttp://xplorestaging.ieee.org/ielx8/10818990/10818991/10819103.pdf?arnumber=10819103-
dc.identifier.fpage1-
dc.identifier.lpage4-
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item.grantfulltextopen-
Appears in Collections:Faculty of Computer Science and Engineering: Conference papers
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