It's odd that the performance gain that you see is so much less than the gain on my machine.
Try putting @time in front of "for w in words" and also in front of "words=...". That will tell you how much time is being spent in each, and whether the limitation is really hashing performance. On Tuesday, March 4, 2014 7:55:12 PM UTC-5, Roman Sinayev wrote: > > I got to about 0.55 seconds with the above suggestions. Still about 2x > slower than Python unfortunately. > The reason I find it necessary for hashing to work quickly is that I use > it heavily for both NLP and when serving data on a Julia webserver. >
