An Agent-based Parallel HPSG Parser for Shared-memory Parallel Machines

Abstract

We describe an agent-based parallel HPSG parser that operates on shared-memory parallel machines. It efficiently parses real-world corpora by using a wide-coverage HPSG grammar. The efficiency is due to the use of a parallel parsing algorithm and the efficient treatment of feature structures. The parsing algorithm is based on the CKY algorithm, in which resolving constraints between a mother and her daughters is regarded as an atomic operation. The CKY algorithm features data distribution and granularity of parallelism. The keys to the efficient treatment of feature structures are i) transferring them through shared-memory, ii) copying them on demand, and iii) writing/reading them simultaneously onto/from memory. Being parallel, our parser is more efficient than sequential parsers. The average parsing time per sentence for the EDR Japanese corpus was 78 msec and its speed-up reaches 13.2 when 50 processors were used.