I did a test where I did this, and increasing the %url population from 1000 to 100_000 caused each algorithm to take over 100 times longer.

the change is:

old: 

my %url = ( monday => { @{[map(($_,1), (1..1000))]} } );

[download]
new: 
my %url = ( monday => { @{[map(($_,1), (1..100_000))]} } );

[download]

I turned the iterations down for the large dataset, because otherwise I would be posting tomorrow instead of today. ;)
results are:

Large dataset
Benchmark: timing 100 iterations of Grep, Max, Ternary...
      Grep: 83 wallclock secs (80.90 usr +  0.02 sys = 80.92 CPU) @  1.24/s (n=100)
       Max: 74 wallclock secs (71.38 usr +  0.00 sys = 71.38 CPU) @  1.40/s (n=100)
   Ternary: 77 wallclock secs (74.15 usr +  0.00 sys = 74.15 CPU) @  1.35/s (n=100)
Small dataset
Benchmark: timing 10000 iterations of Grep, Max, Ternary...
      Grep: 73 wallclock secs (68.50 usr +  0.05 sys = 68.55 CPU) @ 145.88/s (n=10000)
       Max: 61 wallclock secs (57.77 usr +  0.05 sys = 57.82 CPU) @ 172.95/s (n=10000)
   Ternary: 64 wallclock secs (61.54 usr +  0.02 sys = 61.56 CPU) @ 162.44/s (n=10000)

Can you post the rest of the Benchmark output?

Paris Sinclair    |    4a75737420416e6f74686572
pariss@efn.org    |    205065726c204861636b6572
I wear my Geek Code on my finger.

In reply to RE: Re: Algorithm Efficiency vs. Specialized Hardware? by Aighearach
in thread Algorithm Efficiency vs. Specialized Hardware? by Russ

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