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- Title
Accelerating data gravitation-based classification using GPU.
- Authors
Peng, Lizhi; Chen, Yuehui; Yang, Bo; Zhang, Haibo; Hassan, Houcine
- Abstract
Data gravitation-based classification model, a new physic law inspired classification model, has been demonstrated to be an effective classification model for both standard and imbalanced tasks. However, due to its large scale of gravitational computation during the feature weighting process, DGC suffers from high computational complexity, especially for large data sets. In this paper, we address the problem of speeding up gravitational computation using graphics processing unit (GPU). We design a GPU parallel algorithm namely GPU–DGC to accelerate the feature weighting process of the DGC model. Our GPU–DGC model distributes the gravitational computing process to parallel GPU threads, in order to compute gravitation simultaneously. We use 25 open classification data sets to evaluate the parallel performance of our algorithm. The relationship between the speedup ratio and the number of GPU threads is discovered and discussed based on the empirical studies. The experimental results show the effectiveness of GPU–DGC, with the maximum speedup ratio of 87 to the serial DGC. Its sensitivity to the number of GPU threads is also discovered in the empirical studies.
- Subjects
GRAPHICS processing units; MACHINE learning; NEWTON'S law of gravitation; PARTICLE swarm optimization; WAVELET transforms; BIG data; NEURAL circuitry
- Publication
Journal of Supercomputing, 2019, Vol 75, Issue 6, p2930
- ISSN
0920-8542
- Publication type
Article
- DOI
10.1007/s11227-018-2253-5