2026-06-01T18:11:45Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/263972026-02-03T11:14:58Zcom_10630_2254col_10630_37953

SkyFlow: heterogeneous streaming for skyline computation using FlowGraph and SYCL Romero, José Carlos González-Navarro, María Ángeles Rodríguez-Moreno, Andrés Asenjo-Plaza, Rafael Lenguajes de programación Sistemas de informacion Sistemas de soporte a la decisión The skyline is an optimization operator widely used for multi-criteria decision making. It allows minimizing an n-dimensional dataset into its smallest subset. In this work we present SkyFlow, the first heterogeneous CPU+GPU graph-based engine for skyline computation on a stream of data queries. Two data flow approaches, Coarse-grained and Fine-grained, have been proposed for different streaming scenarios. Coarse-grained aims to keep in parallel the computation of two queries using a hybrid solution with two state-of-the-art skyline algorithms: one optimized for CPU and another for GPU. We also propose a model to estimate at runtime the computation time of any arriving data query. This estimation is used by a heuristic to schedule the data query on the device queue in which it will finish earlier. On the other hand, Fine-grained splits one query computation between CPU and GPU. An experimental evaluation using as target architecture a heterogeneous system comprised of a multicore CPU and an integrated GPU for different streaming scenarios and datasets, reveals that our heterogeneous CPU+GPU approaches always outperform previous only-CPU and only-GPU state-of-the-art implementations up to 6.86×and 5.19×, respectively, and they fall below 6% of ideal peak performance at most. We also evaluate Coarse-grained vs Fine-Grained finding that each approach is better suited to different streaming scenarios. 2023-04-24T13:02:01Z 2023-04-24T13:02:01Z 2023-04-24 2022-11-24 journal article Romero, J. C., Navarro, A., Rodríguez, A., & Asenjo, R. (2023). SkyFlow: Heterogeneous streaming for skyline computation using FlowGraph and SYCL. Future Generation Computer Systems, 141, 269-283. https://hdl.handle.net/10630/26397 https://doi.org/10.1016/j.future.2022.11.021 eng http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional Elsevier