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Patel, Janak H. Baxter, Jeffrey John 2014-12-16T22:22:56Z 2014-12-16T22:22:56Z 10000-01-01 1992 1992 In this thesis we explore the problem of resource management for multicomputer systems. A variety of algorithms were developed for different task graph models. We first present a suite of static resource management algorithms. For acyclic graphs, the LAST algorithm provides fast processor allocation and intelligent processor usage. For nondeterministic task graphs, the remap algorithm uses profiling data to iteratively improve the allocation decisions. The remap algorithm provides an efficient, distributed implementation with each processor analyzing the tasks assigned to it. Finally, the template strategy provided a static allocation to dynamic tree-based flow graphs. Next we developed the concept of hybrid resource management, combining static and dynamic strategies. Profiling-based migration uses a migration algorithm to address runtime load imbalances for nondeterministic task costs. The algorithm uses profiled data to make migration decisions with data strictly local to each processor. For dynamic flow graphs, we developed two hybrid resource management techniques, t$\sb-$hybrid, and hybrid2. The t$\sb-$hybrid strategy is a decoupled hybrid strategy, where the static and dynamic portions of the management operate independently from one another. In the hybrid2 strategy, a coupled hybrid strategy, decisions in one strategy affect decisions taken in the other strategy. Made available in DSpace on 2014-12-16T22:22:56Z (GMT). No. of bitstreams: 1 9305464.pdf: 7467541 bytes, checksum: cdbfa35b6b7ad89c318f0e9e21b9423b (MD5) Previous issue date: 1992 Embargo set by: Seth Robbins for item 72141 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs U of I Only 203 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1992. http://hdl.handle.net/2142/71975 (UMI)AAI9305464 Engineering, Electronics and Electrical Computer Science Resource Management for Distributed Memory Multicomputers text Electrical Engineering Electrical Engineering University of Illinois at Urbana-Champaign Dissertation Ph.D.