This paper proposes a method to find the most suitable architecture for a given response time requirement for Example-Retrieval (ER), which searches for the best match from a bulk collection of lingusitic examples. In the Example-Based Approach(EBA), which attains substantially higher accuracy than traditional approaches, ER is extensively used to carry out natural language processing tasks, e.g., parsing and translation. ER, however, is so computationally demanding that it often takes up most of the total sentence processing time. This paper compares several accelerations of ER on different architectures, i.e., serial, MIMD and SIMD. Experimental results reveal the relationship between architectures and response times, which will allows us to find the most suitable architecture for a given response time requirement.