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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