mersenneforum.org - mfaktc: a CUDA program for Mersenne prefactoring

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- - mfaktc: a CUDA program for Mersenne prefactoring (https://www.mersenneforum.org/showthread.php?t=12827)

[QUOTE=storm5510;471234]There is a "no-classes" variant of [I]mfaktc[/I][/QUOTE]I believe you mean the "less-classes" version, which is available in the "extra versions" zip file:
[url]http://download.mersenne.ca/mfaktc/mfaktc-0.21[/url]
[url]http://mersenneforum.org/mfaktc/mfaktc-0.21/[/url]
The regular version of mfaktc uses 4620 classes, the LessClasses version only uses 420.

[QUOTE=James Heinrich;471236]I believe you mean the "less-classes" version, which is available in the "extra versions" zip file:
[URL]http://download.mersenne.ca/mfaktc/mfaktc-0.21[/URL]
[URL]http://mersenneforum.org/mfaktc/mfaktc-0.21/[/URL]
The regular version of mfaktc uses 4620 classes, the LessClasses version only uses 420.[/QUOTE]

I apologize for the error. :blush:

I was not aware there is a fixed number of classes in each. Thank you for the correction and the information. :smile:

I'm setting up TF on a GeForce GTX 1050 in a brand-new computer running Kubuntu. I've downloaded and extracted the mfaktc-0.21 TAR file.

Below is the output. What do I need to do to get this working?

[CODE]mfaktc v0.21 (64bit built)

Compiletime options
THREADS_PER_BLOCK 256
SIEVE_SIZE_LIMIT 32kiB
SIEVE_SIZE 193154bits
SIEVE_SPLIT 250
MORE_CLASSES enabled

Runtime options
SievePrimes 25000
SievePrimesAdjust 1
SievePrimesMin 5000
SievePrimesMax 100000
NumStreams 3
CPUStreams 3
GridSize 3
GPU Sieving enabled
GPUSievePrimes 82486
GPUSieveSize 64Mi bits
GPUSieveProcessSize 16Ki bits
Checkpoints enabled
CheckpointDelay 30s
WorkFileAddDelay 600s
Stages enabled
StopAfterFactor bitlevel
PrintMode full
V5UserID (none)
ComputerID (none)
AllowSleep no
TimeStampInResults no

CUDA version info
binary compiled for CUDA 6.50
CUDA runtime version 6.50
CUDA driver version 9.0

CUDA device info
name GeForce GTX 1050
compute capability 6.1
max threads per block 1024
max shared memory per MP 98304 byte
number of multiprocessors 5
clock rate (CUDA cores) 1455MHz
memory clock rate: 3504MHz
memory bus width: 128 bit

Automatic parameters
threads per grid 655360
GPUSievePrimes (adjusted) 82486
GPUsieve minimum exponent 1055144

running a simple selftest...
ERROR: cudaGetLastError() returned 8: invalid device function[/CODE]

I'm new at Linux but the idea is to make it my main, work computer over time. The more details, the better. :smile:

[QUOTE=Rodrigo;472259]
Below is the output. What do I need to do to get this working?

[CODE]
CUDA version info
binary compiled for CUDA 6.50
CUDA runtime version 6.50
CUDA driver version 9.0

CUDA device info
name GeForce GTX 1050
[/CODE][/QUOTE]

Simple answer: you need to download the CUDA 8 version as the 10xx GPUs do not support older CUDA compilations.

[QUOTE=Rodrigo;472259]I'm setting up TF on a GeForce GTX 1050 in a brand-new computer running Kubuntu. I've downloaded and extracted the mfaktc-0.21 TAR file.

Below is the output. What do I need to do to get this working?

[CODE]mfaktc v0.21 (64bit built)

Compiletime options
THREADS_PER_BLOCK 256
SIEVE_SIZE_LIMIT 32kiB
SIEVE_SIZE 193154bits
SIEVE_SPLIT 250
MORE_CLASSES enabled

Runtime options
SievePrimes 25000
SievePrimesAdjust 1
SievePrimesMin 5000
SievePrimesMax 100000
NumStreams 3
CPUStreams 3
GridSize 3
GPU Sieving enabled
GPUSievePrimes 82486
GPUSieveSize 64Mi bits
GPUSieveProcessSize 16Ki bits
Checkpoints enabled
CheckpointDelay 30s
WorkFileAddDelay 600s
Stages enabled
StopAfterFactor bitlevel
PrintMode full
V5UserID (none)
ComputerID (none)
AllowSleep no
TimeStampInResults no

CUDA version info
binary compiled for CUDA 6.50
CUDA runtime version 6.50
CUDA driver version 9.0

CUDA device info
name GeForce GTX 1050
compute capability 6.1
max threads per block 1024
max shared memory per MP 98304 byte
number of multiprocessors 5
clock rate (CUDA cores) 1455MHz
memory clock rate: 3504MHz
memory bus width: 128 bit

Automatic parameters
threads per grid 655360
GPUSievePrimes (adjusted) 82486
GPUsieve minimum exponent 1055144

running a simple selftest...
ERROR: cudaGetLastError() returned 8: invalid device function[/CODE]I'm new at Linux but the idea is to make it my main, work computer over time. The more details, the better. :smile:[/QUOTE]

Start by checking the -d value in your command line. If you are trying to use the first or only gpu in the system, that's device zero not one. Other things to check are that the gpu driver installed successfully and is the NVIDIA supplied driver, not the one linux will install. (On debian, I found it easier to start over with a Windows install than to resolve that driver type in linux.) In some cases, mfaktc requires a match of the CUDA level between dlls and driver. Review past few pages of posts in this thread for other ideas.

On a gtx1070, I found I needed the cuda levels to match, as follows.
CUDA version info
binary compiled for CUDA 8.0
CUDA runtime version 8.0
CUDA driver version 8.0
That was accomplished by running the less-classes-cuda8 version of mfaktc.

On a gtx480, no such limitation:
CUDA version info
binary compiled for CUDA 6.50
CUDA runtime version 6.50
CUDA driver version 8.0

CUDA device info
name GeForce GTX 480
compute capability 2.0
maximum threads per block 1024
number of multiprocessors 15 (480 shader cores)
clock rate 1451MHz

Thank you @MrRepunit and @kriesel for the valuable info.

I checked and the NVIDIA driver had in fact been properly installed, so I went ahead and downloaded the CUDA 8 version of mfaktc. So far, it's running great, yielding about 248 Ghz-d/day.

Can anyone help in getting mfaktc compiled for Mac for me?

[QUOTE=kriesel;472264] In some cases, mfaktc requires a match of the CUDA level between dlls and driver. [/QUOTE]

Well, not exactly. That was my imprecise interpretation of what I've seen here.

Executable and dlls must match on CUDA levels.
Driver must support at least the CUDA level of the executable and dlls.

Driver must support the compute capability and card type used. (Note v9 driver drops some older types, and later drivers are likely to drop more.)
Only one NVIDIA driver can be loaded at a time, on Windows at least. This can become an issue for systems with a mix of gpu models installed, limiting maximum driver version as well as minimum driver version.

Mfaktc seems to be more particular than CUDALucas or CUDAPm1, in my experience, on GTX10xx. Mfaktc almost runs on CUDA6.5 executables and dlls on GTX1070 with a CUDA 8 driver;
########## testcase 1/2867 ##########
Starting trial factoring M50804297 from 2^67 to 2^68 (0.59 GHz-days)
Using GPU kernel "75bit_mul32_gs"
Date Time | class Pct | time ETA | GHz-d/day Sieve Wait
Sep 11 19:36 | 3387 0.1% | 0.001 n.a. | n.a. 82485 n.a.%
ERROR: cudaGetLastError() returned 8: invalid device function
(program terminates)

I've run benchmarks from CUDA 4.0 up to 8.0 executables on GTX10xx in CUDALucas, and in CUDAPm1, V0.20 CUDA level 5.0 on GTX1070, CUDA 5.5 on GTX1050Ti, CUDA 5.0 on GTX480; mfaktc CUDA 4.2, 6.5 and 8.0 executables on GTX480.

This whole question was a useful review for me, since I've been contemplating branching out from running entirely LL on the GTXxxxx to Mfaktc and CUDAPm1 also. If it saves someone else a little time getting theirs going, so much the better.

I am running CUDA 8.0.90, GPU driver 10.10.14
I had run mfaktc on a Mac in the past with CUDA 6, but wish to run it afresh on NVIDIA GeForce GTX 775M. Not the fastest card about I know, but I 'may' have the opportunity of a breakout box fitted with a 1080ti.

Do I gather that there is NO ONE running mfaktc on a Mac at present.

Does anyone have any compiling skills to help me out with?

Hello! Finally I started TF'ing my exponent by mfaktc-0.21. :)

[URL]http://www.mersenneforum.org/showthread.php?t=21404&highlight=750ti[/URL]

I reported the [I]results.txt[/I] file (from 2^76 to 2^77) and got granted 45.8941 GHz-days. Now I'll run from 2^77 to 2^81. ;)

I'm just wondering: how do you know if someone cheats reporting "no factor" for non-tested exponents? Is there something that I didn't understand?

You don't.
It's very easy to check if someone reports a factor whether it's true or not.
It's impossible to know if there's really no factor in that range without running the whole test again (and then how do we know the person who did the double-check is also telling the truth...)

In my opinion the only way to minimize the problem is to not give credit for TF that doesn't find factors. Just credit finding factors only, make no public record of how much no-factor TF any user does, and there's no incentive to submit false results.

That of course assumes that false no-factor reports in question are malicious. If there is a false report of no-factor in a range due to hardware or software error that's another problem entirely.

In practice, lower ranges of still-unfactored exponents might get another pass of TF in a few decades when the realtime involved is trivial. Previously-missed TF factors have been found in the past by random re-checks.