The effective performance of wide-issue superscalar processors depends on many parameters, such as branch prediction accuracy, available instruction-level parallelism, and instruction-fetch bandwidth. This paper explores the relations between some of these parameters, and more particularly, the requirement in instruction-fetch bandwidth. We introduce new enhancements to boost effectively the instruction-fetch bandwidth of conventional fetch engines. However, experiments strongly show that performance improves less for a given instruction-fetch bandwidth gain as the base bandwidth increases. At the level of bandwidth exhibited by the proposed schemes, the performance improvement is small. This clearly brings to light potential relations between the bandwidth and the other parameters. We provide a model to explain this behavior and quantify some relations. Based on the experimental observation that the available parallelism in an instruction window grows as the square root of the window size, we derive from the model that the instruction fetch bandwidth requirement increases as the square root of the distance between mispredicted branches. We also show that the instruction fetch bandwidth requirement increases linearly with the parallelism available in a fixed-size instruction window. Finally, we review some existing techniques to enhance performance and we describe their impact on the instruction-fetch requirement in the light of the above relations. These techniques include those increasing the amount of instruction-level parallelism (e.g. value-prediction) and those enlarging the effective instruction window (e.g. eager execution).
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