Hello, Happy New Year! I have some fresh benchmark results for you all.
The results are from test runs between the machines thyra (Aarhus, Denmark), bazooka (LA, USA), and serengeti (Trondheim, Norway). I have used benchmark.py, some shell scripting and lots of patience :-)
<<inline: mul-par.png>>
The first graph shows parallel multiplications in the standard 65 bit field. The time is the total time used on the three machines. One can see that bazooka is a bit slower than the two others, but overall they follow each other nicely.
<<inline: mul-par-normalized.png>>
The second graph gives the normalized results from the first graph, that is, the time per multiplication. The time stabilized around 1.5 ms per multiplication when doing more than 2000 multiplications.
<<inline: mul-seq.png>>
If we run things sequential instead, we get the results in graph 3. Here I have only tested up to 1000 multiplications with 10 repetitions since that took 30 minutes.
<<inline: mul-seq-normalized.png>>
Again, we can look at the time per multiplication. I have not good intuition as to why the two fast machines (thyra and serengeti) are able to do the first 10 multiplications much faster than the later ones. The bazooka machine is more stable. I believe these are the most comprehensive results I have made with VIFF so far. For the parallel multiplications I repeated the test at least 100 times and used the median value for the graphs. I took the median value instead of the average value since the median should be more robust when the data has outliers. But since I have no clue about statistics, I would love to hear other suggestions. -- Martin Geisler
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