Re^3: Fast algorithm for 2d array queries

See the referenced thread. I later implemented a partial SSE version as well (merge using SSE, scan not optimized). Result:

Total 301068 elements in 30 vectors
timethis for 5:  5 wallclock secs ( 5.31 usr +  0.00 sys =  5.31 CPU) 
+@ 439.36/s (n=2333)
[download]

Update:

Total 200752 elements in 4 vectors
timethis for 5:  6 wallclock secs ( 5.28 usr +  0.00 sys =  5.28 CPU) 
+@ 1326.33/s (n=7003)
[download]

Update2: Right you are, BrowserUk, I was considering small n case only.

Total 50063728 elements in 1000 vectors
timethis for 5:  6 wallclock secs ( 6.32 usr +  0.00 sys =  6.32 CPU) 
+@  0.63/s (n=4)
[download]

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Re^4: Fast algorithm for 2d array queries by BrowserUk (Patriarch) on Feb 08, 2014 at 00:56 UTC
IMO, that thread is not applicable to this problem. Prove this assertion! With the rise and rise of 'Social' network sites: 'Computers are making people easier to use everyday' Examine what is said, not who speaks -- Silence betokens consent -- Love the truth but pardon error. "Science is about questioning the status quo. Questioning authority". In the absence of evidence, opinion is indistinguishable from prejudice.	[reply]
Re^4: Fast algorithm for 2d array queries by BrowserUk (Patriarch) on Feb 08, 2014 at 09:02 UTC
Let me rephrase my challenge. Are you really suggesting that you can merge sort 500 to 1000 sets of 50,000 numbers, then scan the resulting 2.5 to 5 million element array to count the most frequent constituent -- in 2 milliseconds? With the rise and rise of 'Social' network sites: 'Computers are making people easier to use everyday' Examine what is said, not who speaks -- Silence betokens consent -- Love the truth but pardon error. "Science is about questioning the status quo. Questioning authority". In the absence of evidence, opinion is indistinguishable from prejudice.	[reply]