Time-energy Analysis of Multilevel Parallelism in Heterogeneous Clusters: the Case of EEG Classification in BCI Tasks
Metadatos
Mostrar el registro completo del ítemAutor
Escobar Pérez, Juan José; Ortega Lopera, Julio; Díaz García, Antonio Francisco; González Peñalver, Jesús; Damas Hermoso, MiguelEditorial
Springer Nature
Materia
EEG classification Hybrid programming Time-energy analysis Heterogeneous CPU-GPU parallel architectures Master-worker algorithms Multi-level parallelism
Fecha
2019-06-05Referencia bibliográfica
Escobar, J.J., Ortega, J., Díaz, A.F. et al. Time-energy analysis of multilevel parallelism in heterogeneous clusters: the case of EEG classification in BCI tasks. Journal of Supercomputing 75, 3397-3425 (2019). https://doi.org/10.1007/s11227-019-02908-4
Patrocinador
Spanish Ministerio de Ciencia, Innovación y Universidades under grant PGC2018-098813-B-C31; ERDF fundResumen
Present heterogeneous architectures interconnect nodes including multiple multi-core microprocessors and accelerators that allow different strategies to accelerate the applications and optimize their energy consumption according to the specific power-performance trade-offs. In this paper, a multi-level parallel procedure is proposed to take advantage of all nodes of a heterogeneous CPU-GPU cluster. Two more alternatives have been implemented, and experimentally compared and analyzed from both running time and energy consumption. Although the paper considers an evolutionary master-worker algorithm for feature selection in EEG classification, the conclusions from the experimental analysis here provided can be frequently applied, as many other useful bioinformatics and data mining applications show the same master-worker profile than the classification problem here considered. Our parallel approach allows to reduce the time by a factor of up to 83, with only about a 4.9% of energy consumed by the sequential procedure, in a cluster with 36 CPU cores and 43 GPU compute units.