20220113, 18:55  #89 
Aug 2020
79*6581e4;3*2539e3
503 Posts 
Thanks, the c167 took 12 days of sieving, the c170 had enough relations already after 15 days, much faster than I anticipated also from c150 and c160 jobs on that machine.
Anyway, I tried again with 235M rels and 166.7M uniques which resulted in a matrix: Code:
matrix is 10297670 x 10297895 (4105.4 MB) with weight 1089257460 (105.77/col) sparse part has weight 973222523 (94.51/col) linear algebra completed 309078 of 10297895 dimensions (3.0%, ETA 28h36m) I had anticipated something like 34 weeks WCT for this, now it only took 2.5 weeks. 
20220127, 06:47  #90 
Aug 2020
79*6581e4;3*2539e3
503 Posts 
I am currently testing the optimal qmin for the c170, it seems it's quite large as well. The qrange of the original run was 15M84M yielding 166.4M uniques. In the qrange 24M93M I got 169.0M uniques.
I also have another c165 to factor, should I just use the same parameters as for the c167 discussed here before? 
20220207, 18:33  #91 
"Vincent"
Apr 2010
Over the rainbow
101100010110_{2} Posts 
Are the parametters for polyselect of interest or only the sieving LA and the rest?

20220207, 21:47  #92 
"Vincent"
Apr 2010
Over the rainbow
101100010110_{2} Posts 
ok, I tried a polyselect on the same range, but with different tasks.polyselect.P. on a C165
(7^178*178^71)/129 with Code:
tasks.polyselect.P = 300e3 tasks.polyselect.admin=1.99e6 tasks.polyselect.admax = 2e6 tasks.polyselect.adrange = 1e3 tasks.polyselect.incr = 250 tasks.polyselect.nq = 3125 tasks.polyselect.nrkeep = 6 tasks.polyselect.ropteffort=10 tasks.polyselect.sopteffort=200 Code:
R0: 35809166954067912357846660220632 R1: 13463319667006865269 A0: 2219834428349037570991732730801168691845 A1: 692194239737244432596138760691083 A2: 488342217850668125365542693 A3: 31512898162007566885 A4: 45218104446796 A5: 1994500 skew 3796573.56, size 3.067e016, alpha 6.046, combined = 5.428e013 rroots = 3 Code:
R0: 35750679366989574211105475195855 R1: 633662203321820819083499 A0: 82290745085368826347864212268994400888 A1: 78146700994698691661021326578602 A2: 288482486297396525055747957 A3: 4358229227643201247 A4: 5932124350875 A5: 17995500 skew 1393695.22, size 3.034e016, alpha 6.080, combined = 5.528e013 rroots = 3 Code:
R0: 35809200555754954673312861203782 R1: 31581039660216687989269 A0: 11327319509762555016982123133746333048 A1: 286465213321329043975159328886322 A2: 306077442964448660296883751 A3: 265544123343226564667 A4: 1365778877333728 A5: 4564560000 skew 379204.77, size 1.848e016, alpha 7.335, combined = 4.089e013 rroots = 3 clearly the P=30e6 isn't good for that short range. Last fiddled with by firejuggler on 20220207 at 21:54 
20220207, 22:16  #93  
"Curtis"
Feb 2005
Riverside, CA
5,279 Posts 
Quote:
I'd turn sopteffort way down, too something like 5 to 10 should be best for C160170. Better to search over more a5 values than to look *really* hard at a few. Perhaps you chose these values because you wanted to compare various P values but as it is poly select is such a needleinhaystack game that using poly score as a measure of setting effectiveness is hazy, at best. If you want to compare settings, I suggest you look at the lognorm score posted after stage 1; that's giving you a measure of the quality of the worst poly found before rootopt. It's possible that some settings yield better top polys but worse "worst" polys, but I haven't found such settings yet. Last fiddled with by VBCurtis on 20220207 at 22:16 

20220207, 22:20  #94 
Apr 2020
2BE_{16} Posts 
incr=250 is not a good choice. Better values to try would be 60, 120, 210, 420; you want the leading coefficient to have lots of small prime factors.

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