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In Fig. 9 (a) the same integer workload was run on core 0
at 100%, 90% and 70% CPU core usage levels. Four groups
of dots can be observed: one cluster for the idle state
temperatures and three clusters for the workload
temperatures. The same pattern we can observe for the
relation between CPU usage levels and average CPU
temperatures. There is direct relation between CPU core
temperatures and the CPU time (in terms of usage level) for
the same type of workload, therefore we can estimate the
contribution of the application to the CPU heat generation.
Another aspect we investigated was the influence of
different workload types on CPU cores’ temperatures. Fig.
10 shows the thermal profiles of four workload types:
integer, float, memory and SSE executed successively for
the same amount of time on the same CPU core. Based on
this test, in order to estimate the effect of a CPU intensive
application over the temperature we have to know the type
of operations the application implements.
Temperature [oC]
100
90
80
70
60
50
40
30
20
10
0
0 100 200 300 400 500 600 700 800 900
Time [s]
Integer
Float
Memory
SSE2
Fig. 10. Heat dissipation and power consumption
B. Heat dissipation and Power consumption
Part of the battery energy of a mobile device is
transformed into heat. The increase in temperature enforces
more energy to be consumed. In Fig. 11, power consumption
profile for the previous memory workload is presented.
40000
35000
Battery power consumption
7-9 October 2009, Leuven, Belgium
current profile. This increase of approx. 4W during the
workload execution is due to the heating of the device.
C. Multithreading and Multicore Thermal Profiles
First we run one single workload thread on every CPU
core available in the processor: when the workload was run
on core 0 the plot in Fig. 12 (1) was obtained and when it
was launched on core 1, the plot (2) describes the
temperature variation. When the workload thread set its
affinity to both CPU cores, we obtained the plot (3) in Fig.
12. For this case the operating system schedules the thread
on both cores uneven (in our presented test: 75% core 0 and
25% core 1). It can be also observed that the CPU cores’
temperatures are not equal even if they run the same
workload 100% (Fig. 12 (1) and (2)).
Temperature [oC]
Fig. 12. CPU cores temperatures
The second test presented in Fig. 13 describes the results
of (1) one workload thread executed by one core, (2) two
workload threads executed by one single core and (3) two
threads run by both CPU cores.
Temperature [oC]
120
100
80
60
40
CPU cores temperatures
100
90
80
70
60
50
40
30
20
10
0
0 100 200 300 400 500 600 700 800 900
Time [s]
CPU cores temperatures
(1) Core 0 100%
(2) Core 1 100%
(3) Core 0 75%
(3) Core 1 25%
(1) Core 0 100%
(2) Core 0 100%
(3) Core 0 100%
Power consumption [mW]
30000
25000
20000
15000
10000
5000
0
0 100 200 300 400 500 600 700 800 900
Time [s]
Fig. 11. Heat dissipation and power consumption
During the second phase of the benchmark, when the
workload is applied, the temperature of the processor and
also the temperature of the entire mobile device increase (in
our example the temperature increases from 60 to ~100 o C).
This increase in temperature of has an effect on power
consumption, and a smooth increase (from 30W to 34W)
during phase 2 of the benchmark can be observed in the
20
0
0 100 200 300 400 500 600 700 800 900
Time [s]
Fig. 13. CPU threads temperatures
D. Thermal-Aware Application
We implemented a database application with long
database processing tasks. The application was written in
Visual C++ 2005, uses MS SQL server. The database
processing task was implemented with different thermal
management operations. In Fig. 15 thermal signatures for the
same task workload implemented with different thermal
management techniques are presented. We can reduce
maximum CPU temperature with around 10 o C implementing
DTM at application level (AP) with a decrease in
performance of 40%.
©EDA Publishing/THERMINIC 2009 148
ISBN: 978-2-35500-010-2