Abstract: Synthesizing Variable Instruction Issue Interpreters for Implementing Functional Parallelism on SIMD Computers.

Functional parallelism can be supported on SIMD machines by interpretation. Under such a scheme the programs and data of each task are loaded on the processing elements (PEs) and the Control Unit of the machine executes a central control algorithm that causes the concurrent interpretation of the tasks on the PEs. The central control algorithm is, in many respects, analogous to the control store program on microprogrammed machines. Accordingly, the organization of the control algorithm greatly influences the performance of the synthesized MIMD environment. Most central control algorithms are constructed to interpret the execution phase of all instructions during every cycle (iteration). However, it is possible to delay the interpretation of infrequent and costly instructions to improve the overall performance. Interpreters that attempt improved performance by delaying the issue of infrequent instructions are referred to as variable issue control algorithms. This paper examines the construction of optimized variable issue control algorithms. In particular, a mathematical model for the interpretation process is built and two objective functions (instruction throughput and PE utilization) are defined. The problem of deriving variable issue control algorithms for these criteria has been shown elsewhere to be NP-complete. Therefore, this paper investigates three heuristic algorithms for constructing near optimal variable issue control algorithms. The performance of the algorithms is studied on 4 different instruction sets and the trends of the schedulers with respect to the instruction sets and the objective functions are analyzed.
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