Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Digital Object Identifier (DOI) : 10.14569/IJACSA.2011.020508
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 2 Issue 5, 2011.
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