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 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∗Partially supported by the Seton Hall University Research 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 developed and implemented. We outline the paging algorithm and present an argument of partial correctness. The MT stack page replacement policy is superior to LRU, because it does not incur the expensive time penalties associated with implementing LRU in software.