Name
Abstract | ||
|---|---|---|
Many data mining applications on bioinformatics and bioengineering require solving problems with different profiles from the point of view of their implicit parallelism. In this context, heterogeneous architectures comprised by interconnected nodes with multiple multi-core microprocessors and accelerators, such as vector processors, Graphics Processing Units (GPUs), or Field-Programmable Gate Arrays would constitute suitable platforms that offer the possibility of not only to accelerate the running time of the applications, but also to optimize the energy consumption. In this paper, we analyze the speedups and energy consumption of a parallel multiobjective approach for feature selection and classification of electroencephalograms in Brain Computing Interface tasks, by considering different implementation alternatives in a heterogeneous CPU-GPU cluster. The procedure is able to take advantage of parallelism through message-passing among the CPU-GPU nodes of the cluster (through shared-memory and thread-level parallelism in the CPU cores, and data-level parallelism and thread-level parallelism in the GPU). The experimental results show high code accelerations and high energy-savings: running times between 1.4 and 5.3% of the sequential time and energy consumptions between 5.9 and 11.6% of the energy consumed by the sequential execution. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3235830.3235834 | PBio@EuroMPI |
Field | DocType | Citations |
Graphics,Sequential time,Central processing unit,Implicit parallelism,Feature selection,Computer science,Parallel computing,Brain–computer interface,Energy consumption,Speedup | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juan José Escobar | 1 | 6 | 3.24 |
| Julio Ortega | 2 | 0 | 2.03 |
| Antonio Francisco Díaz | 3 | 0 | 0.34 |
| Jesús González | 4 | 107 | 16.48 |
| M. Damas | 5 | 387 | 33.04 |