Show simple item record Rodrigo, PS Bandara, HMND Perera, S 2018-11-03T00:50:49Z 2018-11-03T00:50:49Z
dc.description.abstract Complex Event Processing (CEP) is a well-known technology in real-time Big Data processing systems. Performance of CEP engines is expected to scale with ever-increasing data rates and complex use cases. CEP operators like stream join and event patterns involve high computational complexity; hence, have a considerable impact on the overall query processing performance. Distributed event processing and CPU-level parallel event processing algorithms are common approaches for improving the performance. We explore how commodity massively parallel architectures like modern Graphics Processing Units (GPUs) can be utilized to improve the performance of frequently used CEP operators. We demonstrate how CEP operators such as event filter, event window, and stream join can be redesigned and implemented on GPUs to gain an order of magnitude improvement in throughput compared to a CPU-based mplementation. This work is demonstrated using NVIDIA CUDA based implementation of CEP operators for Siddhi CEP engine on low-end GPUs. Moreover, this approach reduces event queuing at the incoming event queue, even with a large number of event streams, high arrival rates, and several complex queries. Consequently, the average latency experienced by incoming events is also reduced. en_US
dc.language.iso en en_US
dc.subject Complex Event Processing; Graphics Processing Units; CUDA; Parallelism en_US
dc.title Accelerating complex event processing through GPUs en_US
dc.type Conference-Abstract en_US
dc.identifier.faculty Engineering en_US
dc.identifier.department Department of Computer Science and Engineering en_US
dc.identifier.year 2015 en_US
dc.identifier.conference IEEE 22nd International Conference on High Performance Computing - (HiPC - 2015) en_US
dc.identifier.pgnos pp. 325 - 334 en_US en_US en_US en_US

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