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Old 2022-04-25, 19:40   #45
VBCurtis
 
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Excellent point- we want the ratio of qmin to qmax to be something between 6 and 8. I suspect your C164 with I=14 ran Q roughly 10-60M; since A=28 should yield 40% better, a Q-range of 7-43M might be expected.

So, I agree with Charybdis' suggestion to change qmin to 7M. Good idea!
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Old 2022-04-25, 21:37   #46
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Quote:
Originally Posted by VBCurtis View Post
Excellent point- we want the ratio of qmin to qmax to be something between 6 and 8. I suspect your C164 with I=14 ran Q roughly 10-60M; since A=28 should yield 40% better, a Q-range of 7-43M might be expected.

So, I agree with Charybdis' suggestion to change qmin to 7M. Good idea!
It has been written - it will be done.

One more c164 (231...) has shown up in my work listings and I should be able to start it tomorrow afternoon.
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Old 2022-04-26, 13:39   #47
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The latest is underway:
Code:
N = 231...<164 digits>
tasks.A = 28
tasks.lim0 = 60000000
tasks.lim1 = 40000000
tasks.lpb0 = 31
tasks.lpb1 = 31
tasks.qmin = 7000000
tasks.sieve.adjust_strategy = 2
tasks.sieve.lambda0 = 1.83
tasks.sieve.mfb0 = 58
tasks.sieve.mfb1 = 88
tasks.sieve.ncurves0 = 18
tasks.sieve.ncurves1 = 10
tasks.sieve.qrange = 5000
tasks.sieve.rels_wanted = 175000000
In my reports, is there any interest in the two "filter" lines? I'm thinking of removing them.
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Old 2022-04-26, 15:14   #48
VBCurtis
 
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The filter lines have no effect, since you're using msieve for postprocessing.
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Old 2022-04-26, 16:45   #49
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Quote:
Originally Posted by VBCurtis View Post
The filter lines have no effect, since you're using msieve for postprocessing.
Those were my thoughts, but wondered if they may have any use, anyway. Thanks!
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Old 2022-04-27, 14:29   #50
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The latest c164:
Code:
N = 231... <164 digits>
tasks.A = 28
tasks.lim0 = 60000000
tasks.lim1 = 40000000
tasks.lpb0 = 31
tasks.lpb1 = 31
tasks.qmin = 7000000
tasks.sieve.adjust_strategy = 2
tasks.sieve.lambda0 = 1.83
tasks.sieve.mfb0 = 58
tasks.sieve.mfb1 = 88
tasks.sieve.ncurves0 = 18
tasks.sieve.ncurves1 = 10
tasks.sieve.qrange = 5000
Polynomial Selection (size optimized): Total time: 496571
Polynomial Selection (root optimized): Total time: 31172.8
Lattice Sieving: Total time: 4.28663e+06s (all clients used 4 threads)
Lattice Sieving: Total number of relations: 169728713
Found 119819021 unique, 42731179 duplicate, and 0 bad relations.
cownoise Best MurphyE for polynomial is 8.60775398e-13
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Old 2022-04-27, 18:33   #51
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Looks like a 6% better scoring poly than the last test, but only 4% lower sieve time.
Nearly a wash, but I=14 is also lower memory while being not-slower.
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Old 2022-04-27, 19:30   #52
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Quote:
Originally Posted by VBCurtis View Post
Looks like a 6% better scoring poly than the last test, but only 4% lower sieve time.
Nearly a wash, but I=14 is also lower memory while being not-slower.
Sounds like a suggestion to move back to I=14. I'm thinking I'm going to have to move up or down a digit for my next few tests. Is there a preference if I move to mfb1=89?
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Old 2022-04-28, 01:54   #53
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As long as the input number is close, comparing E-score is pretty accurate (as I did in my previous post). It shouldn't matter whether you go with 163 or 165 or 166 next time for the mfb 89 trial.
Note that a larger mfb "should" improve yield, but more raw relations are likely necessary. The tradeoff is murky- if sec/rel is not better when test-sieving, I go with the smaller mfb. When sec/rel is better, a full factorization is likely to educate us. So, please try a full factorization as you planned.
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Old 2022-04-28, 12:25   #54
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All my runs are full runs, unless they break. At these sizes, full runs are less than 48 hours, so my patience is still holding.
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Old 2022-04-28, 18:05   #55
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I've started a c165. I went back to I=14, but forgot to take qmin back up to 10M. Will that throw things off?:
Code:
N = 309...<165>
tasks.I = 14
tasks.lim0 = 60000000
tasks.lim1 = 40000000
tasks.lpb0 = 31
tasks.lpb1 = 31
tasks.qmin = 7000000
tasks.sieve.adjust_strategy = 2
tasks.sieve.lambda0 = 1.83
tasks.sieve.mfb0 = 58
tasks.sieve.mfb1 = 89
tasks.sieve.ncurves0 = 18
tasks.sieve.ncurves1 = 10
tasks.sieve.qrange = 5000
tasks.sieve.rels_wanted = 175000000
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