20220425, 19:40  #45 
"Curtis"
Feb 2005
Riverside, CA
5634_{10} Posts 
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 1060M; since A=28 should yield 40% better, a Qrange of 743M might be expected.
So, I agree with Charybdis' suggestion to change qmin to 7M. Good idea! 
20220425, 21:37  #46  
"Ed Hall"
Dec 2009
Adirondack Mtns
5,231 Posts 
Quote:
One more c164 (231...) has shown up in my work listings and I should be able to start it tomorrow afternoon. 

20220426, 13:39  #47 
"Ed Hall"
Dec 2009
Adirondack Mtns
5,231 Posts 
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 
20220426, 15:14  #48 
"Curtis"
Feb 2005
Riverside, CA
2×3^{2}×313 Posts 
The filter lines have no effect, since you're using msieve for postprocessing.

20220426, 16:45  #49 
"Ed Hall"
Dec 2009
Adirondack Mtns
5,231 Posts 

20220427, 14:29  #50 
"Ed Hall"
Dec 2009
Adirondack Mtns
5231_{10} Posts 
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 8.60775398e13 
20220427, 18:33  #51 
"Curtis"
Feb 2005
Riverside, CA
1602_{16} Posts 
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 notslower. 
20220427, 19:30  #52 
"Ed Hall"
Dec 2009
Adirondack Mtns
1010001101111_{2} Posts 
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?

20220428, 01:54  #53 
"Curtis"
Feb 2005
Riverside, CA
2×3^{2}×313 Posts 
As long as the input number is close, comparing Escore 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 testsieving, 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. 
20220428, 12:25  #54 
"Ed Hall"
Dec 2009
Adirondack Mtns
5,231 Posts 
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.

20220428, 18:05  #55 
"Ed Hall"
Dec 2009
Adirondack Mtns
5,231 Posts 
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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