Evaluation of non-cubic processor allocation in hybercubes

Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to digital@library.tamu.edu, referencing the URI of the item.Includes bibliographical references: p. 64-67.Issued also on microfiche from Lange Micrographics.The main objective of processor allocation and task mapping in Hypercube multicomputers is to maximize utilization by reducing external and internal fragmentation and to reduce the communication time for messages being exchanged between the processors. Whereas, cubic and contiguous allocation of processors in Hypercube has been well studied, non-cubic and non-contiguous allocations have been largely neglected because of potential message-contention and larger number of hops between source and destination nodes of a message. With current communication techniques, like wormhole routing, delay due to the number of hops between processors is known to be negligible. The goal of this thesis is to evaluate non-contiguous cubic and random allocation and task mapping in Hypercube computers. The degradation in performance due to message-contention and larger average number of hops for messages because of non-contiguity is studied. Emulation experiments are conducted with a synthetic application as well as some actual applications loaded in non-contiguous cubic and random fashion on a 32 node subcube of a Ncube system. The communication time for these applications are measured under varying amount of system load and with varying message sizes. The effects of link contention due to intertask and intratask message interference on the communication time of these applications are observed. It is shown that the increase in communication time of message-passing applications on Ncube due to non-contiguous cubic and random allocation is much smaller compared to a drastic improvement expected in system utilization and simplified task mapping in Hypercube systems. Hence, non-contiguous cubic and random processor allocation and topology independent task assignment can be used in Hypercubes