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Chapter 39½ Implementation and Performance Evaluation of the PDP-1 1 Family 673
4. Measuring the Effect of Design Tradeoffs on Performance
There are two alternative approaches to the problem of determining just how the particular binding of different design decisions affects the performance of each machine:
1 Top-down approach. Attempt to isolate the effect of a particular design tradeoff over the entire space of implementations by fitting the individual performance figures for the whole family of machines to a mathematical model which treats the design parameters as independent variables and performance as the dependent variable.
2 Bottom-up approach. Make a detailed sensitivity analysis of a particular tradeoff within a particular machine by comparing the performance of the machine both with and without the design feature while leaving all other design features the same.
Each approach has its assets and liabilities for assessing design tradeoffs. The first method requires no information about the implementation of a machine, but does require a sufficiently large collection of different implementations, a sufficiently small number of independent variables, and an adequate mathematical model in order to explain the variance in the dependent variable to some reasonable level of statistical confidence. The second method, on the other hand, requires a great deal of knowledge about the implementation of the given system and a correspondingly great amount of analysis to isolate the effect of the single design decision on the performance of the complete system. The information that is yielded is quite exact, but applies only to the single point chosen in the design space and may not be generalized to other points in the space unless the assumptions concerning the machine's implementation are similarly generalizable. In the following subsections the first method is used to determine the dominant tradeoffs and the second method is used to estimate the impact of individual implementation tradeoffs.
4.1 Quantifying Performance
Measuring the change in performance of a particular PDP-11 processor model due to design changes presupposes the existence of some performance metric. Average instruction execution time was chosen because of its obvious relationship to instruction-stream throughput. Neglected are such overhead factors as direct memory access, interrupt servicing, and, on the LSI-11, dynamic memory refresh. Average instruction execution times may be obtained by benchmarking or by calculation from instruction frequency and timing data. The latter method was chosen because of its freedom from the extraneous factors noted above and from the normal clock rate variations found from machine to machine of a given model. This method also allows us to calculate the change in average instruction execution time that would result from some change in the implementation. Such frequency-driven design has already been applied in practice to the PDP-11/60 [Mudge, 1977].
The instruction frequencies are tabulated in Appendix 1 and include the frequencies of the various addressing modes, These figures were calculated from measurements made by Strecker1 on 7.6 million instruction executions traced in 10 different PDP-11 instruction streams encountered in various applications. While there is a reasonable amount of variation of frequencies from one stream to the next, the figures should he representative.
Instruction times were tabulated for each of the eight PDP-11 implementations and reported in Snow and Siewiorek . These times were calculated from the engineering documents for each machine. The times differ from those published in the PDP-11 processor handbooks for two reasons. First, in the handbooks, times have been redistributed among phases to ease the process of calculating instruction times. In Snow and Siewiorek the attempt has been made to accurately characterize each phase. Second, there are inaccuracies in the handbooks arising from conservative timing estimates and engineering revisions. The figures included here may be considered more accurate.
A performance figure is arrived at for each machine by weighting its instruction times by frequency. The results, given in Table 1, form the basis of the analyses to follow.
4.2 Analysis of Variance of PDP-11
Performance: Top-Down Approach
The first method of analysis described above will be employed in an attempt to explain most of the variance in PDP-11 performance in terms of two parameters:
1 Microcycle time. The microcycle time is used as a measure of processor performance which excludes the effect of the memory subsystem.
2 Memory-read-pause time. The memory-read-pause time is defined as the period of time during which the processor clock is suspended during a memory read. For machines with processor/Unibus overlap, the clock is assumed to be turned off by the same microinstruction which initiates the memory access. Memory-read-pause time is used as a measure of the memory subsystem's impact on processor performance. Note that this time is less than the memory access time since all PDP-11 processor clocks will continue to run at least partially concurrently with a memory access.
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