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While MIPS measures the rate of average instructions, MFLOPS is only defined for
the subset of floating-point instructions. An argument against MFLOPS is the fact
that the set of floating-point operations may not be consistent across machines
and therefore the actual floating-point operations will vary from machine to
machine. Yet another argument is the fact that the performance of a machine for
a given program as measured by MFLOPS cannot be generalized to provide a
single performance metric for that machine.
The performance of a machine regarding one particular program might not be
interesting to a broad audience. The use of arithmetic and geometric means are
the most popular ways to summarize performance regarding larger sets of programs
(e.g., benchmark suites). These are defined below.
X
n
1
n
Arithmetic mean ¼
Execution time i
i ¼ 1
n s
Y
n
Geometric mean ¼
Execution time i
i ¼
1
where execution time i is the execution time for the ith program and n is the total
number of programs in the set of benchmarks.
The following table shows an example for computing these metrics.
CPU time on
computer A (s)
CPU time on
computer B (s)
Item
Program 1
50
10
Program 2
500
100
Program 3
5000
1000
Arithmetic mean
1835
370
Geometric mean
500
100
We conclude our coverage in this section with a discussion on what is known as the
Amdahl's law for speedup (SU o ) due to enhancement. In this case, we consider
speedup as a measure of how a machine performs after some enhancement relative
to its original performance. The following relationship formulates Amdahl's law.
Performance after enhancement
Performance before enhancement
SU o ¼
Execution time before enhancement
Execution time after enhancement
Speedup ¼
Consider, for example, a possible enhancement to a machine that will reduce the
execution time for some benchmarks from 25 s to 15 s. We say that the speedup
resulting from such reduction is SU o ¼
25
=
15
¼
1
:
67.
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