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As pipeline depth is increased, the cycle time goes down.
The pipeline depth is calculated for each scenario and the optimal pipeline depth-core combination is derived.
Using such pipeline depth may be efficient for certain programs, but may also lead to optimal pipeline depth for other programs with different behaviors.
These improvements arise from a substantial increase in the pipeline depth (so-called deeper pipelines).
However, these studies focused on fixed pipeline depth during the program execution.
In [4], Kunkel and Smith studied the optimum pipeline depth and defined a set of performance metrics.
Similar(35)
(a) IPS Metric Pipeline depths for various configurations such as 2, 4, 8 and their corresponding pipeline stage unification degrees are 2, 1.5, 1.25 respectively.
(c) EDP Metric The pipeline depths for various configurations such as 2, 4, 8, 16, 32 and their pipeline stage unification VSP degrees are 1, 1, 1.25, 2, and 3.5 respectively.
end{aligned} (15) (b) E-Metric The pipeline depths for various configurations such as 2, 4, 8, 16, 32 and their pipeline stage unification degrees are 1, 1, 1.25, 1.5, and 2 respectively.
To study the effect of different pipeline depths on various cores, we have varied the number of cores of a modern superscalar processor architecture, which has out-of-order execution simulator in the Simple-Scalar Toolset (so-called M-Sim [25]), Table 1 lists the processor configuration and clock frequency assumptions.
An analytical model is also proposed to analyze the relationship between the number of cores and the pipeline stage depth.
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