Optimizing a Semantic Comparator using CUDA-enabled Graphics Hardware Conference Paper uri icon


  • 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. 2011 IEEE.

name of conference

  • 2011 IEEE Fifth International Conference on Semantic Computing

published proceedings


author list (cited authors)

  • Tripathy, A., Mohan, S., & Mahapatra, R.

citation count

  • 4

complete list of authors

  • Tripathy, Aalap||Mohan, Suneil||Mahapatra, Rabi

publication date

  • September 2011