Efficient Breadth First Search on Multi-GPU Systems Using GPU-Centric OpenSHMEM

1. http://graph500.org: Graph 500 benchmark specification 1.2 (2017). http://www.graph500.org/

2. Merrill, D., Garland, M., Grimshaw, A.: Scalable GPU graph traversal. SIGPLAN Not. 47, 117–128 (2012)

   Article  Google Scholar 

3. Bisson, M., Bernaschi, M., Mastrostefano, E.: Parallel distributed breadth first search on the Kepler architecture. CoRR abs/1408.1605 (2014)

   Google Scholar 

4. Potluri, S., Rossetti, D., Becker, D., Poole, D., Gorentla Venkata, M., Hernandez, O., Shamis, P., Lopez, M.G., Baker, M., Poole, W.: Exploring openSHMEM model to program GPU-based extreme-scale systems. In: Gorentla Venkata, M., Shamis, P., Imam, N., Lopez, M.G. (eds.) OpenSHMEM 2014. LNCS, vol. 9397, pp. 18–35. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-26428-8_2

   Chapter  Google Scholar 

5. NVIDIA: GPUDirect (2015). https://developer.nvidia.com/gpudirect

6. NVIDIA: GPUDirect RDMA (2015). http://docs.nvidia.com/cuda/gpudirect-rdma

7. Rossetti, D.: GPUDirect: integrating the GPU with a network interface. In: GPU Technology Conference (2015)

   Google Scholar 

8. Wang, H., Potluri, S., Luo, M., Singh, A.K., Sur, S., Panda, D.K.: MVAPICH2-GPU: optimized GPU to GPU communication for infiniband clusters. Comput. Sci. 26, 257–266 (2011)

   Google Scholar 

9. Potluri, S., Hamidouche, K., Venkatesh, A., Bureddy, D., Panda, D.K.: Efficient inter-node MPI communication using GPUDirect RDMA for infiniband clusters with NVIDIA GPUs. In: Proceedings of the 2013 42nd International Conference on Parallel Processing, ICPP 2013, Washington, DC, USA, pp. 80–89. IEEE Computer Society (2013)

   Google Scholar 

10. MVAPICH: MPI over infiniband, 10GigE/iWARP and RoCE (2015). http://mvapich.cse.ohio-state.edu

11. Aji, A.M., Dinan, J., Buntinas, D., Balaji, P., Feng, W.C., Bisset, K.R., Thakur, R.: MPI-ACC: an integrated and extensible approach to data movement in accelerator-based systems. In: 14th IEEE International Conference on High Performance Computing and Communications, Liverpool, UK (2012)

    Google Scholar 

12. Potluri, S., Bureddy, D., Wang, H., Subramoni, H., Panda, D.K.: Extending openSHMEM for GPU computing. In: Proceedings of the 2013 IEEE 27th International Symposium on Parallel and Distributed Processing, IPDPS 2013, Washington, DC, USA, pp. 1001–1012. IEEE Computer Society (2013)

    Google Scholar 

13. Cunningham, D., Bordawekar, R., Saraswat, V.: GPU programming in a high level language: compiling X10 to CUDA. In: Proceedings of the 2011 ACM SIGPLAN X10 Workshop, X10 2011, pp. 8:1–8:10. ACM, New York (2011)

    Google Scholar 

14. Miyoshi, T., Irie, H., Shima, K., Honda, H., Kondo, M., Yoshinaga, T.: Flat: a GPU programming framework to provide embedded MPI. In: Proceedings of the 5th Annual Workshop on General Purpose Processing with Graphics Processing Units, GPGPU-5, pp. 20–29. ACM, New York (2012)

    Google Scholar 

15. Ueno, K., Suzumura, T.: Parallel distributed breadth first search on GPU. In: 20th Annual International Conference on High Performance Computing, HiPC 2013, Bengaluru (Bangalore), Karnataka, India, 18–21 December 2013, pp. 314–323 (2013)

    Google Scholar 

16. Matsuoka, S.: Making TSUBAME2.0, the world’s greenest production supercomputer, even greener: challenges to the architects. In: Proceedings of the 2011 International Symposium on Low Power Electronics and Design, Fukuoka, Japan, 1–3 August 2011, pp. 367–368 (2011)

    Google Scholar 

17. Bisson, M., Bernaschi, M., Mastrostefano, E.: Parallel distributed breadth first search on the Kepler architecture. IEEE Trans. Parallel Distrib. Syst. 27, 2091–2102 (2016)

    Article  Google Scholar 

18. Pan, Y., Wang, Y., Wu, Y., Yang, C., Owens, J.D.: Multi-GPU graph analytics. CoRR abs/1504.04804 (2015)

    Google Scholar 

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