In the first graph below we plot %CPU utilization versus time for 5
different values of the locality parameter x. It is evident that a certain periodicity in
the utilization of the cpu exists. We noticed this periodicity in all the profilling
quantities that we measured. We did not attempt to fit the data yet, but it seems that a
squared sine or cosine will probably fit the data well.
Graph: CPU utilization vs time
It is also evident the strong correlation between the locality parameter x
and the %cpu utilization.
In the following graph we plot again %CPU utilization versus time, but this time we put
each graph on top of each other in order to better observe the periodicity of the cpu
utilization. Please disregard the Y axis and just observe the patterns.
Graph: CPU utilization vs time
In this graph it is even more evident the periodicity of the cpu utilization. It is
also clear that if we try to fit the data with a squared sine for example we will have to
modulate BOTH PERIOD AND AMPLITUDE.The amplitude seems to grow with increasing x and the
same holds for the period.
In the following graph we plot the absolute number of HARD page faults versus time. We
noticed a strange anomaly. The run with x=0 had less number of page faults than the x=25
run. We do not know how to interpret that yet.
Graph: Hard page faults vs time
In the next 3 graphs we plot hard page fault service time in milisecs versus time. The
first plot includes all 5 runs, the second plot only 2 in order to show more clearly the
periodicity and the third one superimposes all 5 runs on top of each other.
Graph: Hard page fault #1
Graph: Hard page fault #2
Graph: Hard page fault #3
The two final graphs plot resident set size of the test process versus
time. The first graph demonstrates the gradual occupation of memory while the second graph
shows the evolution of the resident set size after the size has saturated at about 45 Mb.
Graph: Memory
Graph: Resident |