Abstract
MapReduce is a programming model introduced by Google for large-scale data processing. Several studies have implemented MapReduce model on Graphic Processing Unit (GPU). However, most of them are based on the single GPU and bounded by GPU memory with inefficient atomic operations. This paper intends to develop a standalone MapReduce system, called MGMR, to utilize multiple GPUs, handle large-scale data processing beyond GPU memory limit, and eliminate serial atomic operations. Experimental results have demonstrated MGMR’s effectiveness in handling large data set.
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References
NVIDIA CUDA Programming Guide 5.0, http://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html
OpenCL - The open standard for parallel programming of heterogeneous systems, http://www.khronos.org/opencl
Caylor, M.: Numerical Solution of the Wave Equation on Dual-GPU Platforms Using Brook+. Presentation, Boise State University (2010)
Dean, J., Ghemawat, S.: MapReduce: Simplified Data Processing on Large Clusters. Communications of the ACM 51(1), 107–113 (2008)
Elteir, M., Lin, H., Feng, W., Scogland, T.: StreamMR: An Optimized MapReduce Framework for AMD GPUs. In: Proceedings of the 21st International Symposium on High-Performance Parallel and Distributed Computing, pp. 364–371 (2011)
Shainer, G., Ayoub, A., Lui, P., Kagan, M., Trott, C., Scantlen, G., Crozier, P.: The development of Mellanox/NVIDIA GPU Direct over InfiniBand a new model for GPU to GPU communications. Computer Science - Research and Development 26(3-4), 267–273 (2011)
White, T.: Hadoop: The Definitive Guide. O’Reilly Media, Inc./ Yahoo Press (2010)
Ranger, C., Raghuraman, R., Penmetsa, A., Bradski, G., Kozyraki, C.: Evaluating MapReduce for Multi-core and Multiprocessor Systems. In: Proceedings of the 2007 IEEE 13th International Symposium on High Performance Computer Architecture, pp. 13–24 (2007)
Fang, W., He, B., Luo, Q., Govindaraju, N.K.: Mars: Accelerating MapReduce with Graphics Processors. In: Proceedings of the 2011 IEEE 17th International Conference on Parallel and Distributed Systems, pp. 608–620 (2011)
Hong, C.T., Chen, D.H., Chen, Y.B., Chen, W.G., Zheng, W.M., Lin, H.B.: Providing Source Code Level Portability Between CPU and GPU with MapCG. Journal of Computer Science and Technology 27(1), 42–56 (2012)
Chen, L., Agrawal, G.: Optimizing MapReduce for GPUs with effective shared memory usage. In: Proceedings of the 21st International Symposium on High-Performance Parallel and Distributed Computing, pp. 199–210 (2012)
Stuart, J.A., Owens, J.D.: Multi-GPU MapReduce on GPU Clusters. In: Proceedings of the 2011 IEEE International Parallel & Distributed Processing Symposium, pp. 1068–1079 (2011)
Alam, S.R., Fourestey, G., Videau, B., Genovese, L., Goedecker, S., Dugan, N.: Overlapping Computations with Communications and I/O Explicitly Using OpenMP Based Heterogeneous Threading Models. In: Proceedings of the 8th International Conference on OpenMP in a Heterogeneous World, pp. 267–270 (2012)
Bell, N., Hoberock, J.: Thrust: A productivity-oriented library for CUDA. In: GPU Computing Gems: Jade Edition, pp. 359–371. Morgan Kaufmann (2011)
Li, X., Lu, P., Schaeffer, J., Shillington, J., Wong, P.S., Shi, H.: On the Versatility of Parallel Sorting by Regular Sampling. Journal of Parallel Computing 19(10), 1079–1103 (1993)
Przydatek, B.: A Fast Approximation Algorithm for the Subset-sum Problem. Journal of International Transactions in Operational Research 9(4), 437–459 (2002)
Yu, S., Tranchevent, L.-C., Liu, X., Glanzel, W., Suykens, J.A.K., De Moor, B., Moreau, Y.: Optimized data fusion for kernel k-means clustering. Journal of IEEE Transactions on Pattern Analysis and Machine Intelligence 34(5), 1031–1039 (2012)
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Chen, Y., Qiao, Z., Jiang, H., Li, KC., Ro, W.W. (2013). MGMR: Multi-GPU Based MapReduce. In: Park, J.J.(.H., Arabnia, H.R., Kim, C., Shi, W., Gil, JM. (eds) Grid and Pervasive Computing. GPC 2013. Lecture Notes in Computer Science, vol 7861. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38027-3_46
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DOI: https://doi.org/10.1007/978-3-642-38027-3_46
Publisher Name: Springer, Berlin, Heidelberg
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