Optimizing a Semantic Comparator using CUDA-enabled Graphics Hardware
Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA
5th IEEE International Conference on Semantic Computing (ICSC), 2011
@article{tripathy2011optimizing,
title={Optimizing a Semantic Comparator using CUDA-enabled Graphics Hardware},
author={Tripathy, A. and Mohan, S. and Mahapatra, R.},
booktitle={5th IEEE International Conference on Semantic Computing (ICSC)},
year={2011}
}
Emerging semantic search techniques require fast comparison of large "concept trees". This paper addresses the challenges involved in fast computation of similarity between two large concept trees using a CUDA-enabled GPGPU co-processor. We propose efficient techniques for the same using fast hash computations, membership tests using Bloom Filters and parallel reduction. We show how a CUDA-enabled mass produced GPU can form the core of a semantic comparator for better semantic search. We experiment run-time, power and energy consumed for similarity computation on two platforms: (1) traditional sever class Intel x86 processor (2) CUDA enabled graphics hardware. Results show 4x speedup with 78% overall energy reduction over sequential processing approaches. Our design can significantly reduce the number of servers required in a distributed search engine data center and can bring an order of magnitude reduction in energy consumption, operational costs and floor area.
October 1, 2011 by hgpu
