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Advances in parallel computation are of central importance to Artificial Intelligence due to the significant amount of time and space their programs require. Functional languages have been identified as providing a clear and concise way of programming parallel machines for artificial intelligence tasks. The problems of exporting, creating, and manipulating processes have been thoroughly studied in relation to the parallelization of functional languages, but none of the necessary support structures needed for the abstraction, like a distributed memory, have been properly designed. In order to design and implement parallel functional languages efficiently, we propose the development of an all-software based distributed virtual memory system designed specifically for the memory demands of a functional language. In this paper, we review the MT architecture and briefly survey the related literature that lead to its development. We then present empirical results obtained from observing the paging behavior of the MT stack. Our empirical results suggest that LRU is superior to FIFO as a page replacement policy for MT stack pages. We present a proof that LRU is an opti

The MT Stack: Paging Algorithm and Performance in a Distributed Virtual Memory System