Parallel Performance Analysis for CUDA-Based Co-rank Framework on Bipartite Graphs Heterogeneous Network

The Co-rank is a ranking algorithm based on bipartite graphs of heterogeneous networks, which has been extensively studied and employed in the past decades. The Co-rank algorithm can utilize all kinds of objects in heterogeneous networks. However, the computational complexity of Co-rank algorithm limits its application on large scale datasets. Considering the efficiency of computation, we parallelize the algorithm with CUDA. We demonstrate our method via large-scale experiments across drug-target datasets and obtain excellent speedup. The results show that the optimization effect of Co-rank on the GPU platform is obvious.

[1]  Wolfgang Müller,et al.  Peer-to-peer technology for interconnecting Web services in heterogeneous networks , 2004, 18th International Conference on Advanced Information Networking and Applications, 2004. AINA 2004..

[2]  Hongyuan Zha,et al.  Co-ranking Authors and Documents in a Heterogeneous Network , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[3]  K. Lund,et al.  Delay and disruption tolerant Web services for heterogeneous networks , 2009, MILCOM 2009 - 2009 IEEE Military Communications Conference.

[4]  Nikolaus A. Adams,et al.  Implementation of a Lattice–Boltzmann method for numerical fluid mechanics using the nVIDIA CUDA technology , 2009, Computer Science - Research and Development.

[5]  Yang Zhang,et al.  Enhancing ESB Based Execution Platform to Support Flexible Communication Web Services over Heterogeneous Networks , 2010, 2010 IEEE International Conference on Communications.

[6]  Hong Chen,et al.  Parallel SimRank computation on large graphs with iterative aggregation , 2010, KDD.

[7]  Guillaume Fertin,et al.  Algorithmic Aspects of Heterogeneous Biological Networks Comparison , 2011, COCOA.

[8]  Yunming Ye,et al.  MultiRank: co-ranking for objects and relations in multi-relational data , 2011, KDD.

[9]  Nitesh V. Chawla,et al.  Link Prediction and Recommendation across Heterogeneous Social Networks , 2012, 2012 IEEE 12th International Conference on Data Mining.

[10]  Feiping Nie,et al.  Trust prediction via aggregating heterogeneous social networks , 2012, CIKM.

[11]  Anisur Rahaman Molla,et al.  Fast Distributed Computation in Dynamic Networks via Random Walks , 2012, DISC.

[12]  Nicholas A. Hamilton,et al.  Fast Parallel Markov Clustering in Bioinformatics Using Massively Parallel Computing on GPU with CUDA and ELLPACK-R Sparse Format , 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[13]  Atish Das Sarma,et al.  Fast Distributed PageRank Computation , 2013, ICDCN.

[14]  Sangkeun Lee,et al.  PathRank: Ranking nodes on a heterogeneous graph for flexible hybrid recommender systems , 2013, Expert Syst. Appl..

[15]  Wahiba Bahsoun,et al.  On ranking relevant entities in heterogeneous networks using a language-based model , 2013, J. Assoc. Inf. Sci. Technol..

[16]  Anisur Rahaman Molla,et al.  Efficient random walk sampling in distributed networks , 2015, J. Parallel Distributed Comput..

[17]  Manish Kumar,et al.  Demonstration of GPGPU-Accelerated Computational Fluid Dynamic Calculations , 2015 .

[18]  Anisur Rahaman Molla,et al.  Distributed computation in dynamic networks via random walks , 2015, Theor. Comput. Sci..

[19]  Jun Li,et al.  Mining Functional Modules in Heterogeneous Biological Networks Using Multiplex PageRank Approach , 2016, Front. Plant Sci..

[20]  Shizhong Xu,et al.  Facilitating Anti-Cancer Combinatorial Drug Discovery by Targeting Epistatic Disease Genes , 2018, Molecules.