An Effective Compact Representation Model for Big Sparse Matrix Multiplication

  • Authors

    • G. Somasekhar
    • K. Karthikeyan
    2018-10-02
    https://doi.org/10.14419/ijet.v7i4.10.20827
  • Big data, Hadoop, Mapreduce, Matrix multiplication, Sparse data, Sparse matrix.
  • The importance of big data is increasing day by day motivating the researchers towards new inventions. Often, a small amount of data is needed to achieve a solution or to draw a conclusion. The new big data techniques stem from the necessity to retrieve, store and process the required data out of huge data volumes. The present paper focuses on dealing with sparse matrices which is fre-quently needed in many sparse big data applications nowadays. It applies compact representation techniques of sparse data and moulds the required data in the mapreducible format. Then the mapreduce strategy is used to get the results quickly which saves execution time and improves scalability. Finally we established that the new algorithm performs well in sparse big data scenario compared to the existing techniques in big data processing.

     

     

  • References

    1. [1] Abadi DJ (2007), Column-stores for wide and sparse data, Proceedings of the third Biennial conference on Innovative Data Sys temsResearch(CIDR), 1-6. Neelima B & Prakash SR (2012), Effective Sparse Matrix Repres- entation for the GPU Architectures. International Journal of Computer Science, Engineering and Applications (IJCSEA) 2, 151-165. Yuster R & Zwick U (2005), Fast sparse matrix multiplication, Journal of ACM Transactions on Algorithms (TALG) 1, 2-13.

      [2] Dean J & Ghemawat S (2008), MapReduce: simplified data pr-ocessing on large clusters. Communications of the ACM 51, 107-113.

      [3] White T (2009), Hadoop: The Definitive Guide, 4th edn. O'Reilly Media, Inc., CA, pp. 3-337.

      [4] Buluc A & Gilbert JR (2011), Parallel Sparse Matrix-Matrix mul-tiplication and Indexing: Implementation and experiments. SIAM Journal on Scientific Computing 34, C170-C191.

      [5] Ballard G, Bulluc A, Demmel J, Grigori L, Lipshitz B, Schwartz & Toledo S (2013), Communication Optimal Parallel Multip-lication of Sparse Random Matrices, Proceedings of the 25th Ann-ual ACM symposium on Parallelism in Algorithms and Architect-ures, 222-231, https://doi.org/10.1145/2486159.2486196

      [6] Smith S, Ravindran N, Sidiropoulos ND & Karypis G (2015), SPLATT: Efficient and Parallel Sparse Tensor-Matrix Multiplica-

      [7] tion, Proceedings of the 29th IEEE International Parallel and Dis-tributed Processing Symposium, 1058-1067, https://doi.org- /10.1109/IPDPS.2015.27

      [8] Seo S, Yoon EJ, Kim J, Jin S, Kim JS & Maeng S (2010), HAMAAn Efficient Matrix Computation with the MapReduce Framew-ork, Proceedings of the IEEE 2nd International Conference onCloud ComputingTechnology and Science(CloudCom), 721-726, https://doi.org/10.1109/CloudCom.2010.17

      [9] Seo S, Jang I, Woo K, Kim I, Kim JS & Maeng S (2009), HPMR: Prefetching and Pre-shuffling in Shared MapReduce Computation

      [10] Environment, Proceedings of the 11th IEEE International Confer-ence on Cluster Computing, 1-8, https://doi.org/10.1109/CLUST-R.2009.5289171

      [11] Myung J & Lee S (2012), Matrix Chain multiplication via multi-way join algorithms in mapreduce, Proceedings of the 6th Interna-tional Conference on Ubiquitous Information Management and Communication, Article No. 53, https://doi.org/10.1145/21847-51.2184817

      [12] Ceccarello M & Silvestri F (2015), Experimental Evaluation of Multi Round Matrix Multiplication on MapReduce, Proceedings of the Meeting on Algorithm Engineering and Experiments (ALE-NEX '15), 119-132, https://doi.org/10.1137/1.9781611973754.11

      [13] Zheng JH, Zhang LJ , Zhu R, Ning K & Liu D (2013), Parall-el Matrix Multiplication Algorithm Based on Vector Linear Com-bination Using MapReduce, Proceedings of the IEEE 9th WorldCongress on Services, 193-200, https://doi.org/10.1109/SERVIC-ES.2013.67

      [14] Somasekhar G & Karthikeyan K (2017), Fast Matrix Multiplicati-on with Big Sparse Data. Cybernetics and Information Technolo-gies 17, 16-30.

      [15] Somasekhar G & Karthikeyan K (2017), The Range based Mapre- duce Algorithm for Large Sparse Matrix Multiplication, Proceedi- ngs of the International Conference on Big Data and Cloud Com- puting, 73-80.

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  • How to Cite

    Somasekhar, G., & Karthikeyan, K. (2018). An Effective Compact Representation Model for Big Sparse Matrix Multiplication. International Journal of Engineering & Technology, 7(4.10), 157-162. https://doi.org/10.14419/ijet.v7i4.10.20827