5751

Optimizing a Semantic Comparator using CUDA-enabled Graphics Hardware

Aalap Tripathy, Suneil Mohan, Rabi Mahapatra
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}

}

Download Download (PDF)   View View   Source Source   

687

views

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.
No votes yet.
Please wait...

* * *

* * *

HGPU group © 2010-2017 hgpu.org

All rights belong to the respective authors

Contact us: