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 2022-10-24, 11:12 #144 kruoli     "Oliver" Sep 2017 Porta Westfalica, DE 132710 Posts QUEUED AS W861 861*2861-1 is a Woodall number that is ready to be sieved as SNFS on 15e: Code: n: 188386046098466199536175452570229431563689849939659262401226219971317243525754666917537583167996559619029043912342710003045776630563860201451094450711950131189856666365670472827943792458379509903998351786742808639630866998718013603 # 861*2^861-1, difficulty: 262.12, anorm: -1.05e+36, rnorm: 1.35e+50 # scaled difficulty: 264.94, suggest sieving rational side # size = 2.269e-13, alpha = 2.805, combined = 4.016e-14, rroots = 2 type: snfs size: 262 skew: 0.29586 c6: 6888 c0: -1 Y1: -1 Y0: 11150372599265311570767859136324180752990208 rlim: 134000000 alim: 266000000 lpbr: 32 lpba: 32 mfbr: 94 mfba: 64 lss: 1 rlambda: 3.9 alambda: 2.7 Test sieving 1 kQ at a time gave Code: Q norm. yield 35M 2465 40M 2257 70M 2194 125M 2069 200M 1680 300M 1597 So I am suggesting sieving Q from 35M to 275M on the rational side for around 460M raw relations. I would like to do the LA. Last fiddled with by swellman on 2022-10-24 at 11:56
 2022-10-24, 12:18 #145 swellman     Jun 2012 3×1,283 Posts QUEUED AS 5m4_865M 5-4,865M from the HCN project is a SNFS 241 (quartic) job to be run on 15e. It is ready for sieving. Code: n: 202555903472921376017494458914423311746291773012577711177576348293001774379027222845952446030626352906737399177339738094628434247569226642258535081891097353893608325736988247777126991974546074562669981652275786573190793761 skew: 0.4472 type: snfs size: 241 c4: 25 c3: 25 c2: 15 c1: 5 c0: 1 Y1: 11972621413014756705924586149611790497021399392059392 Y0: -1292469707114105741986576081359316958696581423282623291015625 rlim: 134000000 alim: 266000000 lpbr: 33 lpba: 32 mfbr: 96 mfba: 64 rlambda: 3.7 alambda: 2.4 Results of test sieving on the rational side with Q in blocks of 1000: Code: MQ Norm_yield 35 1837 50 2261 75 2453 100 2560 150 2447 200 2320 250 2274 300 2187 350 2122 Suggesting a sieving range for Q of 35-350M in order to generate 710M raw relations. Last fiddled with by swellman on 2022-10-24 at 14:57
 2022-11-03, 13:11 #146 swellman     Jun 2012 3·1,283 Posts QUEUED AS 8m7_341 8-7,341 is a SNFS 280 job from the HCN project now ready for sieving on 15e. Code: n: 1257831877902465284401904732297195473572308017725568799120893001756116420424745947122036616596445694485123521851527252735379003753111652033442861028407880668528720791431272129213829740292842857152587040353849000045033642110230877933436752399007747939 # 8^341-7^341, difficulty: 279.96, skewness: 1.00, alpha: 2.22 # cost: 7.57243e+19, est. time: 36059.20 GHz days (not accurate yet!) skew: 1.000 c5: 1 c4: 1 c3: -4 c2: -3 c1: 3 c0: 1 Y1: -1562531701075863192779448904272185314811647640213651456 Y0: 98104607686593128479834996959304746360186153111467503313 rlim: 134000000 alim: 266000000 lpbr: 33 lpba: 33 mfbr: 96 mfba: 66 rlambda: 3.65 alambda: 2.45 Results of test sieving on the rational side with Q in blocks of 1000: Code: MQ Norm_yield 35 1881 50 1940 75 2127 100 2101 150 2085 200 1789 250 1829 300 1666 350 1588 400 1489 450 1553 500 1377 550 1355 600 1276 Suggesting a sieving range for Q of 35-600M to generate 950M raw relations. I will park this job here until more jobs from other projects get posted. Last fiddled with by swellman on 2022-11-07 at 13:31
 2022-11-07, 10:53 #147 kruoli     "Oliver" Sep 2017 Porta Westfalica, DE 1,327 Posts QUEUED AS 145!-1 This C185 is a cofactor of the factorial minus 1 number 145!-1 that is ready to be sieved as GNFS on 15e: Code: n: 61961544527388294841688073882914854435388027897679210391156264443557149767638767403360578514778014603475277763521601393153149828278695416036164441248566103608937469068435239190125514623 type: gnfs skew: 23388742 c5: 28963296 c4: -468307738325310 c3: -49748528932542980322741 c2: 314034469329584823868962434378 c1: 10234603583895446847339037580343693197 c0: 4983288639261044534250632896338765636096980 Y1: 4473326874907429 Y0: -335939328349166369840151777825025122 rlim: 266000000 alim: 134000000 lpbr: 32 lpba: 32 mfbr: 64 mfba: 94 lss: 0 rlambda: 2.7 alambda: 3.9 Test sieving 1 kQ at a time gave Code: Q norm. yield 35M 3344 40M 3227 70M 2997 125M 2634 200M 2283 300M 1970 400M 1675 So I am suggesting sieving Q from 35M to 205M on the algebraic side for around 460M raw relations. I would like to do the LA. Last fiddled with by swellman on 2022-11-07 at 12:30
 2022-11-11, 01:29 #148 RichD     Sep 2008 Kansas 73408 Posts C278 from the MWRB file with OPN 11673. I compromised on the alambda. Increasing it any more generated a few more rels at the cost of time per rels, so I stopped at 2.6. rlambda at 3.6 was much better than 3.5. I remember several years ago one could change the skew a bit to get better yield. I didn’t modify the skew value. The formulaic generation of parameters seems to be pretty good but test sieving is always beneficial, along with other's advice. Please hold for a day or two before queuing to allow feedback and/or modifications. Code: n: 10929875925037315024994112350260780199767161975518955535270167413583164858504212664897476377692335059939499260248762932645204102682318951183447267479739483769724008717453251826937850228654862574004845245478825442381443215504635534927206893532265948445909765759873218837942895601 # 7043^73-1, difficulty: 280.89, skewness: 0.23, alpha: 0.00 # cost: 8.09051e+19, est. time: 38526.25 GHz days (not accurate yet!) skew: 0.228 c6: 7043 c0: -1 Y1: -1 Y0: 14896775084042115440192394458997575421235964401 type: snfs rlim: 134000000 alim: 268000000 lpbr: 33 lpba: 32 mfbr: 96 mfba: 64 rlambda: 3.6 alambda: 2.6 Trial sieving 2K blocks. Code:  Q Yield N-Yld 35M 4173 4576 40M 4523 4251 70M 3876 4011 100M 3808 3759 200M 3008 2914 300M 2541 2528 400M 2075 2277 485M 1949 2119 This is a 32/33 hybrid job. The region should create about 650M relations.
2022-11-11, 13:30   #149
swellman

Jun 2012

384910 Posts

Quote:
 Originally Posted by RichD C278 from the MWRB file with OPN 11673. I compromised on the alambda. Increasing it any more generated a few more rels at the cost of time per rels, so I stopped at 2.6. rlambda at 3.6 was much better than 3.5. I remember several years ago one could change the skew a bit to get better yield. I didn’t modify the skew value. The formulaic generation of parameters seems to be pretty good but test sieving is always beneficial, along with other's advice. Please hold for a day or two before queuing to allow feedback and/or modifications.
A few suggestions:

I believe the lambda should be mfb/log2(lim) + 0.05 which for the algebraic side is closer to 2.33 or 2.35 if you prefer.

For 15e, rlim + alim is limited to 400M per Greg. alim must be changed to 266M.

I’m not sure about your comment on skew. You’ve used the standard skew = |c0/c6|^(1/6). Cownoise recommends shifting skew to a value of 0.29722 but you should confirm this skew value with cownoise and whether if it improves things via test sieving. (I doubt it will help.)

Max had a method of spinning things to lower skew with holding escore (nearly) constant but he’s not been around for awhile now. No one else has his recipe for this technique to my knowledge.

Lastly, you could invert the roots of the polynomial to get a better skew value as suggested for this past job, with @charybdis’s recommendation.

The job as posted should run just fine, but you may want to investigate some of the above ideas. Perhaps others will add deeper insights.

2022-11-11, 13:36   #150
kruoli

"Oliver"
Sep 2017
Porta Westfalica, DE

1,327 Posts

Quote:
 Originally Posted by swellman I believe the lambda should be mfb/log2(lim) + 0.05 which for the algebraic side is closer to 2.33 or 2.35 if you prefer.
IIRC what charybdis said, it should be plus 0.2 or 0.3; I cannot find the post right now.

2022-11-11, 16:24   #151
VBCurtis

"Curtis"
Feb 2005
Riverside, CA

23·3·5·47 Posts

Quote:
 Originally Posted by kruoli IIRC what charybdis said, it should be plus 0.2 or 0.3; I cannot find the post right now.
Charybdis did suggest adding 0.2 to the formula, but I've found that adding 0.05 captures nearly all the relations as adding 0.2, but with faster sec/rel. Dropping the non-sieve-side lambda to 2.35 or 2.4 has gained me 10% or so in job elapsed-time versus my previous "2.6 is good enough" assumption for nearly every job.

More generally, I think too much focus in test-sieving is on improving yield, rather than improving speed. Often both improve at the same time, but with lambda it's worth losing a few relations to find the others faster. I'd test mfba of 63 also; you'll need 4-5% fewer relations to finish the job (like 625M instead of 650M) by shrinking mfba, so if you lose less than 4% in sec/rel it's a net win.

 2022-11-12, 02:12 #152 RichD     Sep 2008 Kansas EE016 Posts The following was performed on a Sandy Bridge chip at Q=40M using 2K block. I need to investigate this further and the other ideas in the coming days. Code:  rlambda=2.6 | rlambda=2.5 | rlambda=2.4 yield sec/rel|yield sec/rel|yield sec/rel 4251 0.519 | 4376 0.521 | 4371 0.520
 2022-11-12, 16:07 #153 kruoli     "Oliver" Sep 2017 Porta Westfalica, DE 1,327 Posts ...and IIUC, enlarging lambda's more than this 0.05 is theoretically useful when the Q range is largely below the corresponding lim? (This would need test sieving over the whole Q range.) Regarding not optimising for rel/s, I plead guilty.
 2022-11-13, 02:19 #154 VBCurtis     "Curtis" Feb 2005 Riverside, CA 160816 Posts Enlarging the lambda *on the sieve side* beyond this 0.05 "extra" is indeed useful, because GGNFS reduces the lim on the sieve side to {start of Q range -1}. So, if you have sieve-side lim of 60M and sieve from 20M-80M, Your lim is actually 19,999,999 for the first sieve range (and 29,999,999 when sieving a range starting at Q=30M, etc) and thus benefits from a larger lambda. This is part of the explanation for why folks who use far-too-big lambdas have test-sieve results with really fast results at small Q (lambda nearly ideal and lower Q are faster anyway) but sec/rel falls off really fast as Q rises (larger Q suffering from lots of wasted cofactor-splitting work due to large lambda). On the non-sieve side, none of this happens / is relevant, so lambda should be simply what theory indicates for lim/MFB choice, with a few hundredths / a tenth added to compensate for the estimation errors that occur in the step where lambda is used. Last fiddled with by VBCurtis on 2022-11-13 at 02:20

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