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There are a growing number of micro-architectures that employ pipelined array units for high-speed floating point applications. To obtain the performance required in such ap plications, effective loop optimization is crucial. The loop optimization problem for micro-architectures with pipelined processing units is similar to the problem of array processor loop optimization.The URPR method has been proven to be an effective, low-complexity approach to optimizing loops in array processor programs, so we conducted a case study of the method applied to a representative pipelined microarchitecture based on the AIUD29500 chip family. The results of applying two LJRPR compaction algorithms and a new compaction algorithm to the 29500-based micr~architecture are presented. With the new compaction algorithm, we were able to realize microcode as efficient as the complex microcode manually derived by AMD.

There are a growing number of micro-architectures that employ pipelined array units for high-speed floating point applications. To obtain the performance required in such ap plications, effective loop optimization is crucial. The loop optimization problem for micro-architectures with pipelined processing units is similar to the problem of array processor loop optimization.The URPR method has been proven to be an effective, low-complexity approach to optimizing loops in array processor programs, so we conducted a case study of the method applied to a representative pipelined microarchitecture based on the AIUD29500 chip family. The results of applying two LJRPR compaction algorithms and a new compaction algorithm to the 29500-based micr~architecture are presented. With the new compaction algorithm, we were able to realize microcode as efficient as the complex microcode manually derived by AMD.

This paper surveys results reported on resource allocation in optimizing microcode compilers. Resource allocation is the phase of microcode generation that binds variables and operators of program text to machine registers and functional units. The first substantial results on resource allocation in optimizing microcode compilers were reported by DeWitt, and subsequent results were reported by Kim and Tan and by Ma and Lewis. We examine each of these methods, focusing on the assumptions they make, how realistic those assumptions are, the algorithms employed and their foundational basis, the perceived difficulty of implementing the algorithms, their effectiveness, and their computational complexity. We conclude by summarizing the results and pointing out important areas for future research, particularly in the context of flow graph microcode generation .