The problem of question/answering (Q/A) is to find answers to open-domain questions by search-ing large collections of documents. Unlike information retrieval systems, very common today in the form of Internet search engines, Q/A systems do not retrieve documents, but instead provide short, relevant answers located in small fragments of text. This enhanced functionality comes with a price: Q/A systems are significantly slower and require more hardware resources than informa-tion retrieval systems. This paper proposes a distributed Q/A architecture that: enhances the sys-tem throughput through the exploitation of inter-question parallelism and dynamic load balancing, and reduces the individual question response time through the exploitation of intra-question par-allelism. Inter and intra-question parallelism are both exploited using several scheduling points: one before the Q/A task is started, and two embedded in the Q/A task. An analytical performance model is introduced. The model analyzes both the inter-question parallelism overhead generated by the migration of questions, and the intra-question parallelism overhead generated by the partitioning of the Q/A task. The analytical model indicates that both question migration and partitioning are required for a high-performance system: intra-questio
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