CUDA-JMI: Acceleration of feature selection on heterogeneous systems
Use este enlace para citar
http://hdl.handle.net/2183/34521
A non ser que se indique outra cousa, a licenza do ítem descríbese como Atribución-NoComercial-SinDerivadas 3.0 España
Coleccións
- GI-GAC - Artigos [193]
Metadatos
Mostrar o rexistro completo do ítemTítulo
CUDA-JMI: Acceleration of feature selection on heterogeneous systemsData
2020-01Cita bibliográfica
J. González-Domínguez, R. R. Expósito, and V. Bolón-Canedo, "CUDA-JMI: Acceleration of feature selection on heterogeneous systems", Future Generation Computer Systems, Vol. 102, pp. 426-436, Jan. 2020, https://doi.org/10.1016/j.future.2019.08.031
É version de
https://doi.org/10.1016/j.future.2019.08.031
Resumo
[Abstract]: Feature selection is a crucial step nowadays in machine learning and data analytics to remove irrelevant and redundant characteristics and thus to provide fast and reliable analyses. Many research works have focused on developing new methods that increase the global relevance of the subset of selected features while reducing the redundancy of information. However, those methods that select features with high relevance and low redundancy are extremely time-consuming when processing large datasets. In this work we present CUDA-JMI, a tool based on Joint Mutual Information that accelerates feature selection by exploiting the computational capabilities of modern heterogeneous systems that contain several CPU cores and GPU devices. The experimental evaluation has been carried out in three systems with different type and amount of CPUs and GPUs using five publicly available datasets from different fields. These results show that CUDA-JMI is significantly faster than its original sequential counterpart for all systems and input datasets. For instance, the runtime of CUDA-JMI is up to 52 times faster than an existing sequential JMI-based implementation in a machine with 24 CPU cores and two NVIDIA M60 boards (four GPUs). CUDA-JMI is publicly available to download from https://sourceforge.net/projects/cuda-jmi
Palabras chave
Feature selection
Machine learning
CUDA
GPU
Multithreading
Machine learning
CUDA
GPU
Multithreading
Descrición
©2019 Elsevier B.V. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/bync-nd/4.0/. This version of the article has been accepted for publication in Future Generation Computer Systems. The Version of Record is available online at https://doi.org/10.1016/j.future.2019.08.031 Versión final aceptada de: J. González-Domínguez, R. R. Expósito, and V. Bolón-Canedo, "CUDA-JMI: Acceleration of feature selection on heterogeneous systemss", Future Generation Computer Systems, Vol. 102, pp. 426-436, Jan. 2020, https://doi.org/10.1016/j.future.2019.08.031
Versión do editor
Dereitos
Atribución-NoComercial-SinDerivadas 3.0 España