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The instruction mixes and the CPI for each instruction type are given below, based on the result
of a program trace experiment:

The average CPI when the program is executed on a uniprocessor with the above trace results is
CPI = 0.6 + (2 * 0.18) + (4 * 0.12) + (8 * 0.1) = 2.24. The corres ponding MIPS rate is (400 * 106)/
(2.24 * 106) ≈ 178.

Another common performance measure deals only with floating-point instructions. These are
common in many scientific and game applications. Floating-point performance is expressed as
millions of floating-point operations per second (MFLOPS), defined as follows:

Number of executed floating− point operations in a program
MFLOPS rate=
Execution time ∗ 106
calculating the mean

In evaluating some aspect of computer system performance, it is often the case that a single
number, such as execution time or memory consumed, is used to characterize performance and

to compare systems. Clearly, a single number can provide only a very simplified view of a system’s
capability. Nevertheless, and especially in the field of benchmarking, single numbers are typically
used for performance comparison [SMIT88]. As is discussed in Section 2.6, the use of benchmarks
to compare systems involves calculating the mean value of a set of data points related to
execution time. It turns out that there are multiple alternative algorithms that can be used for
calculating a mean value, and this has been the source of some controversy in the benchmarking
field. In this section, we define these alternative algorithms and comment on some of their
properties. This prepares us for a discussion in the next section of mean calculation in

benchmarking. The three common formulas used for calculating a mean are arithmetic, geo

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