I've created an SVM in R using the kernlab package, however it's running
incredibly slow (20,000 predictions takes ~45 seconds on win64 R distribution).
CPU is running at 25% and RAM utilization is a mere 17% ... it's not a
hardware bottleneck. Similar calculations using data mining algorithms in SQL
Server analysis services run about 40x faster.
Through trial and error, we discovered that the laplacedot kernel gives us the
best results by a wide margin. Rbfdot is about 15% less accurate, but twice as
fast (but still too slow). The best performance is vanilladot. It runs more or
less instantly but the accuracy is way too low to use.
We'd ideally like to use the laplacedot kernel but to do so we need a
massive speedup. Does anyone have any ideas on how to do this?
Here is some profiling information I generated using rprof. It looks like most
of the time is spent in low level math calls (the rest of the profile consists
of similar data as rows 16-40). This should run very quickly but it looks like
the code is just not optimized (and I don't know where to start). Most
notably, some of the subtraction methods take around .30 seconds... something
must be wrong!
http://pastebin.com/yVPC66Be
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