Accelerating text mining workloads in a mapreduce-based distributed gpu environment

Scientific computations have been using GPU-enabled computers success-fully, often relying on distributed nodes to overcome the limitations of device memory. Only a handful of text mining applications benefit from such infras-tructure. Since the initial steps of text mining are typically data-intensive, and the ease of deployment of algorithms is an important factor in develop-ing 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 effec-tive use of shared memory. We have performed extensive tests of our algo-rithms on a cluster of eight nodes with two NVidia Tesla M2050 attached to each, and we achieve considerable speedups for random projection and self-organizing maps