Abstract
Parallelism or parallel execution is expected to improve the performance of Artificial Intelligence (AI) systems so that they can be applied to much wider areas. One of the major problems with parallelizing AI systems is the lack of methodology for parallel AI programming. This paper discusses key issues in parallel AI programming with parallel Lisp. By implementing two AI systems, OPS5 (a rule-based system) and ATMS (an intelligent database system), three main problems are observed: difficulty in identifying the most time-consuming small tasks, frequent access to global data, and over-sequential execution. Solutions to these problems are presented, including hierarchical decomposition of tasks, runtime control of multiprocessing, reader-writer locks and lazy and speculative computations.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
