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Old 2016-10-23, 17:02   #375
henryzz
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The Bayesian tool will often ask for more ecm. The higher bounds mean a higher chance of finding a factor overall. Sometimes there may be a higher chance of smaller factors being missed but there will be less higher factors missed. More efficiency in finding factors means more ecm is worthwhile often.
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Old 2016-10-26, 14:32   #376
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Quote:
Originally Posted by VBCurtis View Post
I'll contribute 500 @ 85e7 this week,
...
In case you're running these, I'm shifting over to 29e8. I've got just over 3800 reported, as of this morning, at 85e7 with many still assigned that haven't returned. I suppose you could stop at 3-400 and we'd still meet the 4200 suggested. I'll swap the machines still working on 85e7 over and manually get a final count of all of their progress. It might be another hundred.

Edit: I guess I had more stragglers than I thought. I've got about 3960 @ 85e7 for a final count. I've moved everything up. Sorry for the late notice if you've done a bunch of the 500...

Last fiddled with by EdH on 2016-10-26 at 15:10 Reason: Updated info
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Old 2016-10-26, 15:08   #377
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OK, I'll get to 300 and then add a few at 3e9.
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Old 2016-10-26, 17:09   #378
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Quote:
Originally Posted by VBCurtis View Post
OK, I'll get to 300 and then add a few at 3e9.
That sounds good. All my machines are now running 29e8, but they will most assuredly be quite some time doing so. I may retask one or two against other interests. I've some maintenance to perform, as well...
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Old 2016-10-26, 22:26   #379
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How disappointing:
Code:
-> ___________________________________________________________________
-> | Running ecm.py, a Python driver for distributing GMP-ECM work   |
-> | on a single machine.  It is copyright, 2011-2016, David Cleaver |
-> | and is a conversion of factmsieve.py that is Copyright, 2010,   |
-> | Brian Gladman. Version 0.41 (Python 2.6 or later)  3rd Sep 2016 |
-> |_________________________________________________________________|
 
-> Number(s) to factor:
-> 29460893303338144751360360097976743017149046981259832053501450854362438285630845458294329588136961317820466603439061784124252469966169391148524869909406500896547611862071404959591325864761463 (191 digits)
->=============================================================================
-> Working on number: 294608933033381447...959591325864761463 (191 digits)
-> Found previous job file job8854.txt, will resume work...
-> *** Already completed 0 curves on this number...
-> *** Will run 20 more curves.
-> Currently working on: job8854.txt
-> Starting 4 instances of GMP-ECM...
-> ecm -c 5 -maxmem 250 2900000000 < job8854.txt > job8854_t00.txt
-> ecm -c 5 -maxmem 250 2900000000 < job8854.txt > job8854_t01.txt
-> ecm -c 5 -maxmem 250 2900000000 < job8854.txt > job8854_t02.txt
-> ecm -c 5 -maxmem 250 2900000000 < job8854.txt > job8854_t03.txt
 
GMP-ECM 7.0.3 [configured with GMP 6.1.1, --enable-asm-redc] [ECM]
GNU MP: Cannot allocate memory (size=67239952)
GNU MP: Cannot allocate memory (size=125894672)
GNU MP: Cannot allocate memory (size=537395216)

-> *** Error: unexpected return value: -1
It did keep trying many times.

So far, only this one is complaining, though...
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Old 2016-10-26, 22:49   #380
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Please post what B2 value (and k-value) ECM picks for 29e8 with maxmem of 250. That's quite a combination!

I'll post B2, k, and timings for unlimited memory once I have the data. Just fired up 100 3e9 curves.
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Old 2016-10-27, 03:23   #381
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Quote:
Originally Posted by VBCurtis View Post
Please post what B2 value (and k-value) ECM picks for 29e8 with maxmem of 250. That's quite a combination!

I'll post B2, k, and timings for unlimited memory once I have the data. Just fired up 100 3e9 curves.
I'm running ECM via ecm.py and can't find any k-value shown anywhere. All of my machines are using B2=80921447825410.

A lot of them (even with 1400 maxmem) are showing the "GNU MP: Cannot allocate memory (size=########)" message, but they aren't crashing. I tried to downsize to 2 threads @ 500 maxmem each on the aforementioned machine, but it's still "thinking" about it, so I don't have a result yet. I have others running with 450 per thread.
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Old 2016-10-27, 05:33   #382
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You'd have to invoke the -v flag for ECM to spit out the k value; that is, the number of pieces it splits B2 into. Not important, merely my curiosity; I've never probed how large k can get.

Default at 3e9 is k=2, B2=105e12, and peak memory use of 11.3GB. My machine reports 13400 sec for stage 1, 1900 sec for stage 2 (ECM 7.0.1).

Setting k=8 should result in nearly the same B2 with half the memory usage.

Perhaps you're running into a side effect that ECM estimates memory use quite a bit less than peak use. Invoking -v tells me ECM expects to use 8.93GB, but peak use is actually reported by ECM as 11.3GB.
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Old 2016-10-27, 12:59   #383
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Quote:
Originally Posted by VBCurtis View Post
You'd have to invoke the -v flag for ECM to spit out the k value; that is, the number of pieces it splits B2 into. Not important, merely my curiosity; I've never probed how large k can get.

Default at 3e9 is k=2, B2=105e12, and peak memory use of 11.3GB. My machine reports 13400 sec for stage 1, 1900 sec for stage 2 (ECM 7.0.1).

Setting k=8 should result in nearly the same B2 with half the memory usage.

Perhaps you're running into a side effect that ECM estimates memory use quite a bit less than peak use. Invoking -v tells me ECM expects to use 8.93GB, but peak use is actually reported by ECM as 11.3GB.
This number may be out of my reasonable reach. The most memory for any of my machines is 6GB total, running 4 threads. Here's an output from one of the others:
Code:
->=============================================================================
-> Working on number: 294608933033381447...959591325864761463 (191 digits)
-> Currently working on: job3602.txt
-> Starting 2 instances of GMP-ECM...
-> ecm -c 10 -maxmem 1400 2900000000 < job3602.txt > job3602_t00.txt
-> ecm -c 10 -maxmem 1400 2900000000 < job3602.txt > job3602_t01.txt
 
GMP-ECM 7.0.3 [configured with GMP 6.1.1, --enable-asm-redc] [ECM]
GNU MP: Cannot allocate memory (size=537395216)
Using B1=2900000000, B2=81051862041166, polynomial Dickson(30), 2 threads
____________________________________________________________________________
 Curves Complete |   Average seconds/curve   |    Runtime    |      ETA
-----------------|---------------------------|---------------|--------------
     4 of     20 | Stg1 11479s | Stg2  6386s |   0d 21:48:26 |   1d 15:41:33
I suppose I could knock them all down to a single thread. The one machine crashing out with errors didn't work with two threads at 500 maxmem each.

Most of my machines are running headless and if I increase maxmem much more they stop talking to me.

I don't have time today to play with them much. But, maybe later I'll figure something more out... or, I'll move the really weak machines to something they can work with better. I might just not be up to the size of this composite.
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Old 2016-10-27, 14:06   #384
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I have a few machines with big-memory footprints. How about you run stage 1 and I run stage 2? The text files aren't large, can be emailed easily.

You'd invoke ecm with -save residues.txt and bounds 29e8 29e8. The second bound is B2; set equal to B1, ECM won't do any stage 2.

This allows you to make full use of your small-RAM machines, while I can do stage 2 in 1/3rd the time of your machine.

Also, note we're doing a t60 using big-bounds because the Bayesian tool says so; you can simply choose to do a t60 the old-fashioned way, say with B1 = 3e8.

Last fiddled with by VBCurtis on 2016-10-27 at 14:17
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Old 2016-10-28, 03:36   #385
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Let's try your suggestion. I got home a bit late, but am attempting to shift all my machines over to see where they are in the morning. I will be issuing the following to each:
Code:
python ecm.py -c 20 -save residues${USER}.txt 2900000000 2900000000 <ecmIn
This will result in a single residue file written to by all threads on a particular machine. Will this be OK, or does each thread need its own residue file? If this is OK, can I also combine residue files from multiple machines when I send them to you?

Thanks for helping me learn new parts of this.
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