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108 Cray T3E User’s Guide
This equation can be normalized by setting W
1
+ W
p
= 1. Here W
1
= α
(the sequential portion) and W
p
= 1 α (the parallel portion). Now you
get
S
p
=
1
α + (1 α)/p
.
For example, if you have a program which contains a 10 % sequential
part the equation reads
S
p
=
1
0.1 + 0.9/p
.
Setting p →∞, you get the maximum speedup, which is 1/0.1 = 10.
Therefore, the sequential part starts to dominate, when you add more
processors.
Efficiency e measures how well the code is parallellized:
e =
S
p
p
.
In the best case the efficiency is 1 and we say that the scalability is linear.
Ambdahls’ law gives a rather pessimistic picture of scalability. In many
cases it is not necessary to keep the model size fixed when doing parallel
computing. This way, the parallel part of the program (1 α)canbe
increased at the same time you add more processors.
Gustafson’s law specifies a different scalability concept: you do not keep
the model size fixed instead, you keep the solution time fixed. This
means that you want to solve the largest problem size possible, given a
time limit. A typical case is weather forecasting: you want to get a 24-
hour forecast within one hour, since the value of this forecast decreases
rapidly as time goes by.
Gustafson’s scaling law can be expressed as follows:
S
0
p
=
W
1
+ pW
p
W
1
+ W
p
.
Note that the time on p processors is compared to the time it would take
to compute this task on one processor. By normalizing (W
1
+ W
p
= 1, as
above) you find
S
0
p
= p α
0
(p 1).
Suppose that you have 128 processors available. Now,
S
0
128
= 128 α
0
(128 1) = 128 127α
0
.
If the sequential part is α
0
= 0.1, you obtain a speedup of 128 12.7 =
115.3. If α
0
= 0.05, you get a speedup of about 122. However, note
that the bigger model size might actually not fit in the memory of one
processor, so you most probably are not able to do the comparison runs.
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