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.
Similar content being viewed by others
References
Beyeler M, Oros N, Dutt N et al (2015) A GPU-accelerated cortical neural network model for visually guided robot navigation. Neural Netw 72:75–87
Cano A, Olmo JL, Ventura S (2013) Parallel multi-objective ant programming for classification using GPUs. J Parallel Distrib Comput 73(6):713–728
Cano A, Zafra A, Ventura S (2014) Parallel evaluation of Pittsburgh rule-based classifiers on GPUs. Neurocomputing 126(27):45–57
Cano A, Zafra A, Ventura S (2013) Weighted data gravitation classification for standard and imbalanced data. IEEE Trans Cybern 43(6):1672–1687
Chen Z, Xiong R, Cao J (2016) Particle swarm optimization-based optimal power management of plug-in hybrid electric vehicles considering uncertain driving conditions. Energy 96:197?08
Cotter A, Srebro N, Keshet J (2011) A GPU-tailored approach for training kernelized SVMs. In: The 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp 805–813
Cover TM, Hart PE (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21–27
Eberhart RC, Shi Y (1998) Comparison between genetic algorithms and particle swarm optimization. Evolutionary Programming VII, Heidelberg. Springer, Berlin, pp 611–616
Franco MA, Bacardit J (2016) Large-scale experimental evaluation of GPU strategies for evolutionary machine learning. Inf Sci 330(10):385–402
Hassan R, Cohanim B, Weck O et al (2005) A comparison of particle swarm optimization and the genetic algorithm. In: The 1st AIAA Multidisciplinary Design Optimization Specialist Conference
Ho T, Lam P, Leung C (2008) Parallelization of cellular neural networks on GPU. Pattern Recogn 41(8):2684–2692
Huqqani AA, Schikuta E, Ye S et al (2013) Multicore and GPU parallelization of neural networks for face recognition. Proced Comput Sci 18:349–358
Kennedy J (1999) Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance. In: The 1999 Congress of Evolutionary Computation, vol 3. pp 1931–1938
Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: The 1995 IEEE International Conference on Neural Networks, pp 1942–1948
Khronos Group: The open standard for parallel programming of heterogeneous systems (2013). http://www.khronos.org/opencl/
Krink T, Vesterstrom J, Riget J (2002) Particle swarm optimization with spatial particle extension. In: The 2002 IEEE Congress on Evolutionary Computation, pp 1474–1479
Li Q, Salman R, Test E et al (2013) Parallel multitask cross validation for support vector machine using GPU. J Parallel Distrib Comput 73(3):293–302
Leung CS, Wong TT, Lam PM et al (2006) An RBF-based image compression method for image-based rendering. IEEE Trans Image Process 15(1):1031–1041
NVIDIA: CUDA C Programming Guide (2012). http://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html
Panda S, Padhy NP (2008) Comparison of particle swarm optimization and genetic algorithm for FACTS-based controller design. Appl Soft Comput 8(4):1418–1427
Parsazad S, Yazdi HS, Effati S (2013) Gravitation based classification. Inf Sci 220:319–330
Peng L, Yang B, Chen Y, Abraham A (2009) Data gravitation based classification. Inf Sci 179(6):809–819
Peng L, Zhang H, Yang B et al (2014) A new approach for imbalanced data classification based on data gravitation. Inf Sci 288(20):347–373
Prabhu RD (2008) SOMGPU: an unsupervised pattern classifier on graphical processing unit. In: The 2008 IEEE Congress on Evolutionary Computation, pp 1011–1018
Pratap P, Bhatia RS, Kumar B (2016) Design and simulation of equilateral triangular microstrip antenna using particle swarm optimization (PSO) and advanced particle swarm optimization (APSO). Sādhanā 41(7):721–725
Reyes O, Morell C, Ventura S (2016) Effective lazy learning algorithm based on a data gravitation model for multi-label learning. Inf Sci 340–341:159–174
Simić D, Tanackov I, Gajić V et al (2009) Financial forecasting of invoicing and cash inflow processes for fair exhibitions. Hybrid artificial intelligence systems. Springer, Berlin, pp 686–693
Tan K, Zhang J, Du Q et al (2015) GPU parallel implementation of support vector machines for hyperspectral image classification. IEEE J Sel Top Appl Earth Obs Remote Sens 8(10):4647–4656
UCI Machine Learning Repository. http://archive.ics.uci.edu/ml/
Weiss SM, Kulikowski CA (1991) Computer systems that learn: classification and prediction methods from statistics. Neural nets, Machine learning, and expert systems. Morgan Kaufmann, San Maeto
Wen G, Wei J, Wang J et al (2013) Cognitive gravitation model for classification on small noisy data. Neurocomputing 118:245–252
Wong TT, Leung CS, Heng PA et al (2007) Discrete wavelet transform on consumer-level graphics hardware. IEEE Trans Multimed 9(3):668–673
Yang J, Lu L, Ouyang W et al (2017) Estimation of kinetic parameters of an anaerobic digestion model using particle swarm optimization. Biochem Eng J 120:25–32
Yoshida H, Kawata K, Fukuyama Y, Takayama S, Nakanishi Y (2000) A particle swarm optimization for reactive power and voltage control considering voltage security assessment. IEEE Trans Power Syst 15:1232–1239
Acknowledgements
This research was partially supported by the National Natural Science Foundation of China Under Grant Nos. 61472164, 61573166, 61572230, 61672262, and 61373054, the National Basic Research Program of China (973 Program) Under Grant No. 2013CB29602, the Doctoral Fund of University of Jinan Under Grant Nos. XBS1623, and XBS1523.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Peng, L., Zhang, H., Hassan, H. et al. Accelerating data gravitation-based classification using GPU. J Supercomput 75, 2930–2949 (2019). https://doi.org/10.1007/s11227-018-2253-5
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11227-018-2253-5