Characterization of Branch and Data Dependencies in Programs for Evaluating Pipeline Performance

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Abstract
The nature by which branches and data dependencies generate delays that degrade pipeline performance is investigated in this paper. We show that for the general execution trace, few specific delays can be considered in isolation; rather, the magnitude of any specific delay may depend on the relative proximity of other delays. This phenomenon can make the task of accurately characterizing a trace tape with simple statistics intractable. We present a set of trace reductions that facilitates this task by simplifying the corresponding data-dependency graph. The reductions operate on multiple data-dependency arcs and branches in conjunction; those arcs whose performance implications are redundant with respect to the dependency graph are identified, and eliminated from the graph. We show that the reduced graph can be accurately characterized by simple statistics. We use these statistics to show that as the length of a pipeline increases, the performance degradation due to data dependencies and branches increases monotonically. However, lengthening the pipeline may correspond to decreasing the cycle time of the pipeline. These two opposing effects are used in conjunction to derive an equation for optimal pipeline length for a given trace tape. The optimal pipeline length is shown to be characterized by n = √γα where γ is the ratio of overall circuit delay to latching overhead, and a is a function of the trace statistics that accounts for the delays induced by data dependencies and branches.

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