Accelerating text mining workloads in a MapReduce-based distributed GPU environment

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.

► Entirely MapReduce-based, flexible and scalable text mining workflow.
► A GPU-based distributed random indexer.
► A GPU version of a MapReduce-based self-organizing map algorithm.
► The speedup of a workflow of up to 10×.