Octave vs Python

"Don't do loops in Octave." is a well known truth. But sometimes loops are just too handy  or cannot be avoided at all. I was curious whether there is a difference in execution time of loops in Python and Octave since both are interpreted languages.

tic;
for i=1:100
for j=1:100
for k=1:100
  vain1 = i^2+j^2+k^2;
  vain2 = i^2+j^2+k^2;
  ...
  vain10 = i^2+j^2+k^2;
endfor
endfor
endfor
t = toc;
disp(num2str(t));

The following two scripts do nothing, but executing 3 nested loops with 100 iterations each and doing some useless computation within the loops. One script is in Python and one is in Octave. The Octave script is also listed above. Octave version 3.6.4 and Python version 2.7.3 was used for the comparison. The results are devastating.

user@machine:~/scripts/python_vs_octave$ octave loops.m
...
39.48
...
user@machine:~/scripts/python_vs_octave$ python loops.py
3.10390710831

Even in this simple example, the Octave script takes more than 10 times as long as the equivalent Python script. The difference becomes even bigger, if the amount of computation inside the loops is increased. Since Python also comes with sophisticated matrix processing capabilities (NumPy, SciPy) and if severe performance degradation for more sophisticated numerical analysis is not acceptable, the proverb above can be simply shortened to "Don't do Octave."


PS.: Another popular data analysis software is R. On the same machine using R version 2.14.1, the execution time of the equivalent script was 18.35s, which is in between the execution time of the Python and Octave scripts.

Characterize CPU Cooling

Did you ever want to find out how good your PC's cooling system does under stress? Maybe you have just bought/built a PC and you want to find out if it keeps sufficiently cool.

The temperatures of the individual cores can be obtained easily with the lm-sensors package. To put a little stress on the CPU, cpuburn is very helpful.

Here is a little Python script that records the temperatures and frequencies of the CPU over time and another Octave script that visualizes the temperature data. The package cpufrequtils is required to obtain the CPU frequencies. For validation purposes, mpstat is used to record the CPU utilization. If you benchmark your CPU with a program that does not fully utilize the CPU during certain periods of time, this data might be helpful to correct for strange effects in the temperature curve.

Assuming a CPU with 2 cores and using burnP6 to create some stress , the script might be executed as follows:
$python rec_sensor_log 5000 sensors_log.csv & burnP6 & burnP6

The first parameter is the time between two temperature samples in milliseconds and the second is the name of the output file. Depending on the amount of available cores in the CPU, several instances of burnP6 (or equivalent) should be started. The script parses the output from lm-sensors in a not very sophisticated way. If you have more/less cores, you will probably have to make modifications to the script.

The visualization script (vis_log.m) will prompt for the input filename. Frequency scaling might occur at high temperatures. If this is detected, a vertical line is drawn into the plot.

Here is a sample plot that was created during a quick test on my laptop. Under stress the temperature rises up to approx. 70°C and the fan spins faster to keep the temperature at about this level. As soon as the stress is removed, the temperature quickly falls below 50°C.