2026-09-19T13:15:47Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/286202022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Saman Amarasinghe.
Swenson, Shane Michael, 1979-
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2005-09-27T17:21:28Z
2005-09-27T17:21:28Z
2002
2002
http://hdl.handle.net/1721.1/28620
57562293
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.
Includes bibliographical references (leaves 89-91).
Instruction scheduling on software exposed architectures, such as Raw, must be performed in both time and space. The complexity and variance of application scheduling regions dictates that the space-time scheduling task be divided into phases. Unfortunately, the interaction of phases presents a phase ordering problem. In this thesis, the structure of program scheduling regions is studied. The scheduling regions are shown to have varying characteristics that are too diverse for a single simple algorithm to cover. A new scheduling technique is proposed to cope with this diversity and minimize the phase ordering problem. First, rather than maintaining exact mappings of instructions to time and space, the internal state of the scheduler maintains probabilities for different assignments of instructions to time and space resources. Second, a set of small scheduling heuristics cooperatively iterate over the probabilistic assignments many times in order to minimize the effects of phase ordering. A simple spatial instruction scheduler for Raw machines based on this technique is implemented and shown to outperform existing spatial scheduling systems on average.
by Shane Michael Swenson.
M.Eng.
91 leaves
3796872 bytes
3806701 bytes
application/pdf
application/pdf
en_US
Massachusetts Institute of Technology
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Spatial instruction scheduling for raw machines
Thesis
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