Generating Performance Analysis of GPU compared to Single-core and Multi-core CPU for Natural Language Applications

Abstract: In Natural Language Processing (NLP) applications, the main time-consuming process is string matching due to the large size of lexicon. In string matching processes, data dependence is minimal and hence it is ideal for parallelization. A dedicated system with memory interleaving and parallel processing techniques for string matching can reduce this burden of host CPU, thereby making the system more suitable for real-time applications. Now it is possible to apply parallelism using multi-cores on CPU, though they need to be used explicitly to achieve high performance. Recent GPUs hold a large number of cores, and have a potential for high performance in many general purpose applications. Programming tools for multi-cores on CPU and a large number of cores on GPU have been formulated, but it is still difficult to achieve high performance on these platforms. In this paper, we compare the performance of single-core, multi-core CPU and GPU using such a Natural Language Processing application.

Shubham Gupta and M.Rajasekhara Babu. “Generating Performance Analysis of GPU compared to Single-core and Multi-core CPU for Natural Language Applications”. International Journal of Advanced Computer Science and Applications (IJACSA) 2.5 (2011). http://dx.doi.org/10.14569/IJACSA.2011.020508

@article{Gupta2011,
title = {Generating Performance Analysis of GPU compared to Single-core and Multi-core CPU for Natural Language Applications},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2011.020508},
url = {http://dx.doi.org/10.14569/IJACSA.2011.020508},
year = {2011},
publisher = {The Science and Information Organization},
volume = {2},
number = {5},
author = {Shubham Gupta and M.Rajasekhara Babu}
}