Local Microcode Compaction Techniques
ACM Computing Surveys (CSUR)
A non-deterministic scheduler for a software pipelining compiler
MICRO 25 Proceedings of the 25th annual international symposium on Microarchitecture
Very long instruction work architectures and the ELI-512
25 years of the international symposia on Computer architecture (selected papers)
Very Long Instruction Word architectures and the ELI-512
ISCA '83 Proceedings of the 10th annual international symposium on Computer architecture
Paper: A boltzmann machine approach to code optimization
Parallel Computing
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Effective optimization of FPS Array Processor assembly language (APAL) is difficult. Instructions must be rearranged and consolidated to minimize periods during which the functional units remain idle or perform unnecessary tasks. Register conflicts and branches cause complications. Deterministic algorithms to arrange instructions traditionally use complex heuristics which are tailored to specific inputs. A non-deterministic approach can be simpler and effective on a large class of inputs. This is a progress report on the “Monte Carlo” optimizer under construction at Cornell University by the authors. This optimizer randomly modifies the text of an APAL program without changing its meaning. Modifications which improve the program are favored. A set of six elementary transformations are the basis for modifications.