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Accelerating text mining workloads in a MapReduce-based distributed GPU environment
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DOI：10.1016/j.jpdc.2012.10.001.png)
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Abstract
En 中文
Scientific computations have been using GPU-enabled computers successfully, often relying on distributed nodes to overcome the limitations of device memory. Only a handful of text mining applications benefit from such infrastructure. Since the initial steps of text mining are typically data intensive, and the ease of deployment of algorithms is an important factor in developing advanced applications, we introduce a flexible, distributed, MapReduce-based text mining workflow that performs I/O-bound operations on CPUs with industry-standard tools and then runs compute-bound operations on GPUs which are optimized to ensure coalesced memory access and effective use of shared memory. We have performed extensive tests of our algorithms on a cluster of eight nodes with two NVidia Tesla M2050s attached to each, and we achieve considerable speedups for random projection and self-organizing maps. (C) 2012 Elsevier Inc. All rights reserved.
Keywords:
GPU computing
MapReduce
Text mining
Self-organizing maps
Random projection
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