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

Advances in parallel computation are of central importance to Artificial Intelligence due to the significant amount of time and space their pro- grams 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 paralleliza- tion of functional languages, but none of the necessary support structures needed for the ab- straction, like a distributed memory, have been properly designed. In order to design and im- plement parallel functional languages efficiently, we propose the development of an all-software based distributed virtual memory system de- signed 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 observ- ing 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- ?Partially supported by the Seton Hall University Re- search Council. †Partially supported by NSF grant CDA-9114481. ‡Partially supported by NSF grant HRD-9703600. mal page replacement policy. Based on this proof the MT stack page replacement policy was de- veloped and implemented. We outline the paging algorithm and present an argument of partial cor- rectness. The MT stack page replacement policy is superior to LRU, because it does not incur the expensive time penalties associated with imple- menting LRU in software.