ECM for CUDA GPUs in latest GMP-ECM ?

[lorgix]

Originally Posted by bsquared

No... but possibly a competing technology
No promises...

I'm gonna hold you to that.

[xilman]

Hmm. I tried to run an 11M test on my 460 but this appeared

pcl@anubis $ ecm -timestamp -save timer -gpu 11000000 0
GMP-ECM 7.0.1-dev [configured with GMP 6.1.0, --enable-asm-redc, --enable-gpu] [ECM]
3129333620258940486330629139630925933631807344645219843266754670086579137577512492047980091055198974792305750155580720314468906941756462912147587701714558353912867079793467240454311904331638921
Input number is 3129333620258940486330629139630925933631807344645219843266754670086579137577512492047980091055198974792305750155580720314468906941756462912147587701714558353912867079793467240454311904331638921 (193 digits)
[Fri Sep 29 12:09:41 2017]
Using B1=11000000, B2=0, sigma=3:2396433671-3:2396433894 (224 curves)
GPU: Block: 32x32x1 Grid: 7x1x1 (224 parallel curves)
cudakernel.cu(256) : Error cuda : too many resources requested for launch.
pcl@anubis $

It used to work but something has clearly changed in the last few months. A reboot and full investigation will follow but for now I can report a single curve on the host processor, a AMD Phenom(tm) II X6 1090T clocked at 3.2Ghz, took took 29760ms.

Historical data from my lab book indicates that 224 curves at B1=110M used to take 529.7s cpu and 24645.4s gpu, which suggests that the B1=11m benchmark should take around 2500 seconds.

The system with a 970 is currently running a GNFS matrix and that should finish before benchmarking takes place.

[wombatman]

Originally Posted by xilman

Hmm. I tried to run an 11M test on my 460 but this appeared

pcl@anubis $ ecm -timestamp -save timer -gpu 11000000 0
GMP-ECM 7.0.1-dev [configured with GMP 6.1.0, --enable-asm-redc, --enable-gpu] [ECM]
3129333620258940486330629139630925933631807344645219843266754670086579137577512492047980091055198974792305750155580720314468906941756462912147587701714558353912867079793467240454311904331638921
Input number is 3129333620258940486330629139630925933631807344645219843266754670086579137577512492047980091055198974792305750155580720314468906941756462912147587701714558353912867079793467240454311904331638921 (193 digits)
[Fri Sep 29 12:09:41 2017]
Using B1=11000000, B2=0, sigma=3:2396433671-3:2396433894 (224 curves)
GPU: Block: 32x32x1 Grid: 7x1x1 (224 parallel curves)
cudakernel.cu(256) : Error cuda : too many resources requested for launch.
pcl@anubis $

It used to work but something has clearly changed in the last few months. A reboot and full investigation will follow but for now I can report a single curve on the host processor, a AMD Phenom(tm) II X6 1090T clocked at 3.2Ghz, took took 29760ms.

Historical data from my lab book indicates that 224 curves at B1=110M used to take 529.7s cpu and 24645.4s gpu, which suggests that the B1=11m benchmark should take around 2500 seconds.

The system with a 970 is currently running a GNFS matrix and that should finish before benchmarking takes place.

I had a similar issue on my 560. I think I had to edit the header file that defined how many curves were run by default. If you use "-gpucurves 112", does it still fail?

[xilman]

Originally Posted by wombatman

I had a similar issue on my 560. I think I had to edit the header file that defined how many curves were run by default. If you use "-gpucurves 112", does it still fail?

I haven'tyet investigated. the point is that until recently the default 224 curves ran just fine. "Recently" a number of other changes have been made. This is a Gentoo system and most anything graphical could be using a different amount of resources since it was rebuilt.

[xilman]

Originally Posted by xilman

The system with a 970 is currently running a GNFS matrix and that should finish before benchmarking takes place.

The 970 worked fine:

pcl@horus ~ $ ecm -timestamp -save timer -gpu 11000000 0
GMP-ECM 7.0.2-dev [configured with GMP 6.1.0, --enable-asm-redc, --enable-gpu, --enable-assert] [ECM]
3094431532040564572408601477248844996578265879041533103613101760833878317954758837772212892619722197393217347727674267786666231717141855759707447284275839786370629363307
Input number is 3094431532040564572408601477248844996578265879041533103613101760833878317954758837772212892619722197393217347727674267786666231717141855759707447284275839786370629363307 (169 digits)
[Sun Oct  1 10:59:47 2017]
Using B1=11000000, B2=0, sigma=3:346811657-3:346812488 (832 curves)
GPU: Block: 32x32x1 Grid: 26x1x1 (832 parallel curves)
Computing 832 Step 1 took 98070ms of CPU time / 3220596ms of GPU time

Yes, it's a different number but the run time is independent of the input because the code uses constant time constant size arithmetic.

The numbers indicate 118ms of cpu and 3871ms of gpu per curve. A single curve on the host cpu (i7-5820 @ 3.3GHz) took 22906ms so a naive calculation gives that the gpu is about 5.7 times the speed of a single cpu core.


The 460 system still dies in the same way despite a reboot and with "-gpucurves 112" so more investigation is required.

[WraithX]

Originally Posted by xilman

cudakernel.cu(256) : Error cuda : too many resources requested for launch.

The 460 system still dies in the same way despite a reboot and with "-gpucurves 112" so more investigation is required.

This error was brought up on the ecm-discuss mailing list back in July 2016. I'm not sure what changed in the code to make it use more resources, but some older cards fail with this error message. A workaround was put into the README.gpu file:

4. Known issues

On some configurations (GTX 570 with compute capability 2.0 for example)
one gets the Cuda error "too many resources requested for launch". This
can be solved by decreasing ECM_GPU_CURVES_BY_BLOCK from 32 to 16 in ecm-gpu.h.

You can see the the threads from July 2016 here:
https://lists.gforge.inria.fr/piperm...ly/thread.html
(relevant thread is "[Ecm-discuss] CC 2.0 issue with GTX 570" )

And the last one came in August 2016, here:
https://lists.gforge.inria.fr/piperm...st/thread.html

[xilman]

Originally Posted by WraithX

This error was brought up on the ecm-discuss mailing list back in July 2016. I'm not sure what changed in the code to make it use more resources, but some older cards fail with this error message. A workaround was put into the README.gpu file:

4. Known issues

On some configurations (GTX 570 with compute capability 2.0 for example)
one gets the Cuda error "too many resources requested for launch". This
can be solved by decreasing ECM_GPU_CURVES_BY_BLOCK from 32 to 16 in ecm-gpu.h.

You can see the the threads from July 2016 here:
https://lists.gforge.inria.fr/piperm...ly/thread.html
(relevant thread is "[Ecm-discuss] CC 2.0 issue with GTX 570" )

And the last one came in August 2016, here:
https://lists.gforge.inria.fr/piperm...st/thread.html

Many thanks! Given that information I can go back and see what happened to cause the problem and, perhaps, fix it in a more computationally efficient manner.

[lorgix]

B1=11e6 takes 3076 seconds on my Tesla M2050. 448 curves. (c290)
Does anybody have a recent binary for CC 2.0 btw?

[storm5510]

Originally Posted by wombatman

I truthfully don't remember all the steps I went through to get everything compiled successfully with VS2012 (I have no experience with VS2015). I'm attaching the gpu_ecm exe I have built. Try it out and see what dlls you need for it.

I have a GTX-1080 with the latest driver set running on Windows 10 Pro x64 v1903. This would not work with it. I gave me the message below:

The application was not able to start correctly. (0xc000007b). Click OK to close this application.

I am running the latest CPU variant with no problems, so this is not a big issue, to me anyway.

[EdH]

In trying to spin up a GPU version of GMP-ECM in the Colab Environment:

. . .
configure: Using cuda.h from /usr/include/linux/include
checking cuda.h usability... yes
checking cuda.h presence... yes
checking for cuda.h... yes
checking that CUDA Toolkit version is at least 3.0... no
configure: error: a newer version of the CUDA Toolkit is needed

apt search cuda-toolkit

Sorting... Done
Full Text Search... Done
cuda-toolkit-10-0/unknown,now 10.0.130-1 amd64 [installed,automatic]
  CUDA Toolkit 10.0 meta-package

cuda-toolkit-10-1/unknown 10.1.243-1 amd64
  CUDA Toolkit 10.1 meta-package

nvidia-cuda-toolkit/bionic 9.1.85-3ubuntu1 amd64
  NVIDIA CUDA development toolkit

BTW, I installed the 10.1 and 9.1... toolkits also, with no success.

Suggestions, anyone?

[Dylan14]

Originally Posted by EdH

In trying to spin up a GPU version of GMP-ECM in the Colab Environment:

. . .
configure: Using cuda.h from /usr/include/linux/include
checking cuda.h usability... yes
checking cuda.h presence... yes
checking for cuda.h... yes
checking that CUDA Toolkit version is at least 3.0... no
configure: error: a newer version of the CUDA Toolkit is needed

apt search cuda-toolkit

Sorting... Done
Full Text Search... Done
cuda-toolkit-10-0/unknown,now 10.0.130-1 amd64 [installed,automatic]
  CUDA Toolkit 10.0 meta-package

cuda-toolkit-10-1/unknown 10.1.243-1 amd64
  CUDA Toolkit 10.1 meta-package

nvidia-cuda-toolkit/bionic 9.1.85-3ubuntu1 amd64
  NVIDIA CUDA development toolkit

BTW, I installed the 10.1 and 9.1... toolkits also, with no success.

Suggestions, anyone?

Hmm... could you try cat cuda.h to see what version it is?
If it is larger than 3 (which is most likely is) then you may have to fiddle around with the configure script so it looks for a higher version.