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Accelerate the Execution of Graph Processing Using GPU

  • Shweta Nitin Aher
  • Sandip M. Walunj
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 106)

Abstract

Graph data structure is a collection of vertices and edges. Graph is utilized to model objects in social network and web graph. In practical computing many applications are work with large-scale graphs. Large graphs are composed of wide range vertices with billions of edges. It became challenging to process these large graphs. The Graphics Processing Unit (GPU) is an electronic circuit which is used to increase the performance. GPU used with various graph algorithms for faster the execution of large graph. However, it is difficult to process large graph due to millions of vertices, edges and irregularities of graph structures. Pregel and Medusa are programming frameworks which was developed to process large graph. Pregel framework works in iterations. It was developed to solve the graph problem in parallel computing. Medusa framework provides API for ease of programming and hides the programming complexity. However, these systems have a problem of irregular access to memory and load imbalance. To simplify graph processing problem, the proposed system will use GPU with Shortest Path algorithm. SSSP, APSP, and BFS algorithms are used to handle large graphs. The proposed system will use GPU’s shared memory for great performance and less computing time. Use of shared memory will help to resolve the problem of irregular memory access. To minimize the data transfer time among CPU and GPU system will use pinned memory and by batching many small transfer into single transfer.

Keywords

Large-scale graph GPU Nvidia CUDA Shortest path algorithm Graph processing 

References

  1. 1.
    Coll Ruiz, O., Matsuzaki, K.: s6raph: Vertex-Centric Graph Processing Framework with Functional Interface. ACM (2016)Google Scholar
  2. 2.
    Zhong, W., Sun, J., Chen, H., Xiao, J., Chen, Z., Chang, C., Shi, X.: Optimizing graph processing on GPUs. IEEE Trans. Parallel Distrib. Syst. (2016)Google Scholar
  3. 3.
    Gaikar, S., Prof. Walunj, S.: An automated system to accelerate image reconstruction using GPU. Int. J. Emerg. Trends Technol. (IJETT) 3(2) (2016)Google Scholar
  4. 4.
    Pisal, T., Shrimali, A., Gautam, O., Patil, L., Walunj, S.M., Satoshi: Acceleration of CUDA programs for non-gpu users using cloud. IEEE (2015)Google Scholar
  5. 5.
    Nikam, A., Nara, A., Paliwal, D., Prof. Walunj, S.M.: Acceleration of drug discovery process on GPU. IEEE (2015)Google Scholar
  6. 6.
    Mahale, K., Kanaskar, S., Kapadnis, P., Desale, M., Prof. Walunj, S.M.: Acceleration of game tree search using GPGPU. IEEE (2015)Google Scholar
  7. 7.
    Patta, R.A., Kurup, A.R., Bajad, H.S., Walunj, S.M.: Augmenting speed of SQL database operations using NVIDIA GPU. Int. Res. J. Eng. Technol. 02(03) (2015)Google Scholar
  8. 8.
    Patta, R.A., Kurup, A.R., Walunj, S.M.: Enhancing speed of SQL database operations using GPU. IEEE (2015)Google Scholar
  9. 9.
    Desale, T.R.: Parallelizing graph algorithms on GPU for optimization. IOSR J. Comput. Eng. (IOSR-JCE) 17, 57–63 (2015)Google Scholar
  10. 10.
    Lai, S., Lai, G., Shen, G., Jin, J., Lin, X.: GPregel: A GPU-based parallel graph processing model. In: 17th International Conference on High Performance Computing and Communications. IEEE (2015)Google Scholar
  11. 11.
    Guo, Y., Lucia Varbanescu, A., Iosup, A., Epema, D.: An empirical performance evaluation of GPU-enabled graph-processing systems. In: IEEE/ACM International Symposium on Cluster (2015)Google Scholar
  12. 12.
    Khorasani, F., Gupta, R., Bhuyan, L.N.: Scalable SIMD-efficient graph processing on GPUs. In: International Conference on Parallel Architecture and Compilation (2015)Google Scholar
  13. 13.
    Shirahata, K., Sato, H., Matsuoka, S.: Out-of-core GPU memory management for MapReduce based large-scale graph processing. IEEE (2014)Google Scholar
  14. 14.
    Zhong, J., He, B.: Medusa: simplified graph processing on GPUS. IEEE Trans. Parallel Distrib. Syst. 25(6), 1543–1552 (2013)CrossRefGoogle Scholar
  15. 15.
    Zhong, J., He, B.: Towards GPU accelerated large-scale graph processing in the cloud. In: IEEE International Conference on Cloud Computing Technology and Science (2013)Google Scholar
  16. 16.
    Han, W.-S., Lee, S., Park, K., Lee, J.-H., Kim, M.-S., Kim, J., Yu, H.: Turbograph: a fast parallel graph engine handling billion-scale graphs in a single pc. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 77–85. ACM (2013)Google Scholar
  17. 17.
    Di Pierro, M.: OpenCL programming using Python syntax. ACM (2013)Google Scholar
  18. 18.
    Shirahata, K., Sato, H., Suzumura, T., Matsuoka, S.: A GPU implementation of generalized graph processing algorithm GIM-V. IEEE Int. Conf. Cluster Comput. (2012)Google Scholar
  19. 19.
    Dashora, S., Khare, N.: Implementation of graph algorithms over GPU: a comparative analysis. In: 2012 IEEE Students’ Conference on Electrical, Electronics and Computer Science (2012)Google Scholar
  20. 20.
    Hong, S., Kim, S.K., Oguntebi, T., Olukotun, K.: Accelerating CUDA graph algorithms at maximum warp. ACM 46(8), 267276 (2011)Google Scholar
  21. 21.
    Malewicz, G., Austern, M.H., Bik, A.J., Dehnert, J.C., Horn, I., Leiser, N., Czajkowski, G.: Pregel: a system for large-scale graph processing. In: Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, pp. 135–146. ACM (2010)Google Scholar
  22. 22.
    Agarwal, V., Petrini, F., Pasetto, D., Bader, D.: Scalable graph exploration on multicore processors. In: Proceedings of the ACM/IEEE SC 2010 Conference on SupercomputingGoogle Scholar
  23. 23.
    Kartz, G.J., Kider Jr., J.T.: All-pairs shortest-paths for large graphs on the GPU. In: ACM SIGGRAPH/EUROGRAPHICS Symposium on Graphics Hardware (2008)Google Scholar
  24. 24.
    Harish, P., Narayanan, P.J.: Accelerating large graph algorithms on the GPU using CUDA. In: Proceedings of HiPC (2007)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Sandip Institute of Technology and Research CentreNashikIndia

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