Re^2: Can you improve upon my algorithm.

As to the complexity, (to me) it looks very much like O(n^2) in time.

I agree. the Perl version certainly looks to be tending that way with respect to the time for a number of records with the number of buffers fixed.

But the number of buffers also has a non-linear effect that is harder to quantify.

These are timings taking using the Perl prototype:

Array               N u m b e r   o f   b u f f e r s.
Size                2               4               8               16
+        
10000     9.710932970    14.441396952    16.980458975     17.980459929
20000    38.186435938    54.618164063    65.470575094     68.913941145
40000   148.056374073   224.696855068   244.073187113    260.217727184
80000   310.495392084   882.082360983   969.694694996   1098.488962889
[download]

But Perl's internal overheads tend to cloud the issue.

These are a similar set of timings, (though with 1000 times as many records), from a straight Perl to C conversion:

Array                 N u m b e r   o f   b u f f e r s.
Size                  2               4               8              1
+6
 10000000   0.248736811     0.497881829     0.799180768     1.33560723
+0
 20000000   0.285630879     1.024963784     1.879727758     3.58831668
+6
 40000000   0.402336371     1.211978628     3.943654854     7.76231127
+6
 80000000   0.966428312     2.093228984    10.674847289    15.20866750
+8
160000000   3.682530644     7.499381800    16.773807549    34.45496560
+7
[download]

That's a lot less pessimistic of the algorithm.

Based on the timings alone, it looks to be linear(ish) in the number of buffers and (um) log.quadratic in the number of records?

With the rise and rise of 'Social' network sites: 'Computers are making people easier to use everyday'

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Re^3: Can you improve upon my algorithm. by bitingduck (Deacon) on Mar 12, 2015 at 15:01 UTC
Based on the timings alone, it looks to be linear(ish) in the number of buffers and (um) log.quadratic in the number of records? That sounds consistent with what the wikipedia article says about in place merge sorting: O(Nlog^2(N)), but with a "reference needed" note, so it might be empirical or folklore.	[reply]