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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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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