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All issues Volume 208 (2018) MATEC Web Conf., 208 (2018) 05002 References
Open Access
Issue
MATEC Web Conf.
Volume 208, 2018
2018 3rd International Conference on Measurement Instrumentation and Electronics (ICMIE 2018)
Article Number 05002
Number of page(s) 6
Section Computer Science and Intelligent Technology
DOI https://doi.org/10.1051/matecconf/201820805002
Published online 26 September 2018
  1. X. Li, C.H Li. and Y. Xie. 2011. “A Retrieval System of Vehicles Based on Recognition of License Plates”. Proceedings of 2011 International Conference on Machine Learning and Cybernetics (ICMLC), IEEE, Guilin, pp.1453–1459. [CrossRef] [Google Scholar]
  2. Hoshino, T., Maruyama, N., Matsuoka, S. and Takaki, R. 2013. “CUDA vs OpenACC: Performance Case Studies with Kernel Benchmarks and a memory-bound CFD Application”. 13th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), pp. 136–143. [Google Scholar]
  3. Herdman, J.A.Gaudin, W.P. Mclntosh-Smith, S. and Boulton, M. 2012. “Accelerating Hydrocodes with OpenACC, OpenCL and CUDA”. 2012 SC Companion: High Performance Computing, Networking, Storage and Analysis (SCC). pp. 465–471. [CrossRef] [Google Scholar]
  4. Christgau, S., Spazier, J., Schnor, B., Hammitzsch, M., Babeyko, A. and Waechter, J. 2014. “A comparison of CUDA and OpenACC: Accelerating the Tsunami Simulation EasyWave”. 27th International Conference on Architecture of Computing Systems (ARCS). pp. 1–5. [Google Scholar]
  5. Che, S., Jeremy, W., Sheaffer, Michael, B., Lukasz G. S., Liang. W., and Kevin, S. 2010. “A characterization of the rodinia benchmark suite with comparison to contemporary cmp workloads”, in Proceedings of the IEEE International Symposium on Workload Characterization (IISWC’10), pp. 1–11 [Google Scholar]
  6. Fang, J., Varbanescu, A and Sips, H. 2011. “A Comprehensive Performance Comparison of CUDA and OpenCL”. International Conference of Parallel Processing (ICPP), pp. 216–225. [Google Scholar]
  7. Krste, A. Ras, B. Bryan, C. Joseph, G. Parry, H. Kurt, K. David, P. William, P. John, S. Samuel, W. and Katherine, Y. 2006. “The landscape of parallel computing research: a view from Berkeley”. Tech. Rep. UCB/EECS-2006-183, Electrical Engineering and Computer Science, University of California at Berkeley [Google Scholar]
  8. The Open ACC Application Programming Interface, Version1.0, November 2011. [Google Scholar]
  9. Kamran, K. Neil, D. and Firas, H. A Performance Comparison of CUDA and OpenCL. [Google Scholar]
  10. http://aqua.dwavesys.com [Google Scholar]
  11. Po-Yu Chen ; Chun-Chieh Lan ; Long-Sheng Huang and Kuo-Hsuan Wu. Overview and Comparison of OpenCL and CUDA Technology for GPGPU. IEEE Asia Pacific Conference on Circuits and Systems (APCCAS). pp. 448 – 451. Dec. 2012 [Google Scholar]
  12. Calvin, M, Jeffrey, O. and Xuechao, L. Autotuning OpenACC Work Distribution via Direct Search.To appear at Extreme Science and Engineering Discovery Enviroment (XSEDE15), Jul. 2015 [Google Scholar]
  13. CUDA, “NVIDIA CUDA [online]. available: http://developer.nvidia.com/category/zone/cuda-zone”, 2012. [Google Scholar]
  14. OpenACC. https://www.openacc.org/ [Google Scholar]
  15. PGI Accelerator, “The Portland Group, PGI Fortran and C Accelarator Programming Model [Online]. Available: http://www.pgroup.com/resources/accel.htm”, 2009 [Google Scholar]
  16. HMPP, “HMPP Workbench, a directive-based compiler for hybrid computing [Online]. Available: www.caps-entreprise.com/hmpp.html”, 2009. [Google Scholar]
  17. J. C. Beyer, E. J. Stotzer, A. Hart, and B. R. de Supinski, “OpenMP for Accelerators.” in IWOMP’11, 2011, pp. 108–121 [Google Scholar]
  18. T. D. Han and T. S. Abdelrahman, “hicuda: High-level gpgpu programming”, IEEE Transactions on Parallel and Distributed Systems, vol. 22, no. 1, pp. 78–90, 2011 [CrossRef] [Google Scholar]
  19. A. Leung, N. Vasilache, B. Meister, M. Baskaran, D. Wohlford, C. Bastoul, and R. Lethin, “A mapping path for multi-GPGPU accelerated computers from a portable high level programming abstraction”, in Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units, ser. GPGPU ’10. New York, NY, USA: ACM, 2010, pp. 51–61 [CrossRef] [Google Scholar]
  20. J. Dongarra, P. Beckman, T. Moore, P. Aerts, G. Aloisio, J.-. Andre, D. Barkai, J.-. Berthou, T. Boku, B. Braunschweig, F. Cappello, B. Chapman, X. Chi, A. Choudhary, S. Dosanjh, T. Dunning, S. Fiore, A. Geist, B. Gropp, Robert Harrison, M. Hereld, M. Heroux, A. Hoisie, K. Hotta, Y. Ishikawa, Z. Jin, F. Johnson, S. Kale, R. Kenway, D. Keyes, B. Kramer, J. Labarta, A. Lichnewsky, T. Lippert, B. Lucas, B. Maccabe, S. Matsuoka, P. Messina, P. Michielse, B. Mohr, M. Mueller, W. Nagel, H. Nakashima, M. E. Papka, D. Reed, M. Sato, E. Seidel, J. Shalf, D. Skinner, M. Snir, T. Sterling, R. Stevens, F. Streitz, B. Sugar, S. Sumimoto, W. Tang, J. Taylor, R. Thakur, A. Trefethen, M. Valero, A. van der Steen, J. Vetter, P. Williams, R. Wisniewski, and K. Yelick, “The International Exascale Software Project RoadMap”, Journal of High Performance Computer Applications, vol. 25, no. 1, 2011 [Google Scholar]

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