Rambling way off topic!

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Rambling way off topic!

Absolutely and is making this thread to long.

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Greetings from TJ

I've a GTX660 (I5; windows 7 64bit ) that I use only for crunching and I'm thinking about upgrading to a GTX970.
Is worth to buy a GTX970 G1 Gaming or is preferable a GTX 970 W.F.(30 € cheaper) ?

In the Skgiven table the GTX 980 has a performance of 109% while GTX 970 has 93%.
Is worth to buy a GTX 980 or is best value a GTX 970 (34% cheaper) ?
Thanks for your advice.

A GTX 970 is a significant upgrade from a GTX660 (80% faster for here), while only using an additional 5W power.

The GTX980 is a bigger GPU and uses 25W more than your GTX660 (if that might be an issue). It's about 17% faster than a GTX970, but costs 34% more, so while it's a bit more powerful it's not quite as good value for money.

It's down to personal choice, but I would go for the 970 and save the €30 too.

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8/1/2015 7:38:20 AM | | CUDA: NVIDIA GPU 0: GeForce GTX 780 (driver version 353.62, CUDA version 7.5, compute capability 3.5, 3072MB, 2956MB available, 4698 GFLOPS peak)

Make change to program to run 2 or more video cards in the same computer.

In the event viewer find the data directory.
8/1/2015 7:38:20 AM | | Starting BOINC client version 7.4.42 for windows_x86_64
8/1/2015 7:38:20 AM | | log flags: file_xfer, sched_ops, task
8/1/2015 7:38:20 AM | | Libraries: libcurl/7.39.0 OpenSSL/1.0.1j zlib/1.2.8
8/1/2015 7:38:20 AM | | Data directory: C:\ProgramData\BOINC

C:programfiles(x86)/BOINC

<cc_config>
<use_all_gpus>1</use_all_gpus>
</cc_config>


Simply make sure it says to use all gpus

Make change to program to run 2 or more video cards in the same computer.

In the event viewer find the data directory.
8/1/2015 7:38:20 AM | | Starting BOINC client version 7.4.42 for windows_x86_64
8/1/2015 7:38:20 AM | | log flags: file_xfer, sched_ops, task
8/1/2015 7:38:20 AM | | Libraries: libcurl/7.39.0 OpenSSL/1.0.1j zlib/1.2.8
8/1/2015 7:38:20 AM | | Data directory: C:\ProgramData\BOINC

C:programfiles(x86)/BOINC

<cc_config>
<use_all_gpus>1</use_all_gpus>
</cc_config>


Simply make sure it says to use all gpus

My computer (running 64-bit Windows 7) doesn't have a C:programfiles(x86)/BOINC directory. It does, however, have a C:/Program Files/BOINC directory. What file within that directory should get the cc_config addition? Is anything else needed if the file doesn't already exist?

Google "BOINC client configuration"
Click the link that comes up
If the webserver is down, then on the Google results page, click the little down-arrow, and then click Cached.

... and try to keep this thread on-topic, please.

I run 64 bit server 2008. I have both 32 bit and 64 bit boinc folders.
The config file is in both folders. Here is my 64 bit boinc folder.
<cc_config>
<use_all_gpus>1</use_all_gpus>
</cc_config>


Both Boinc folders have the "Use All Gpus" in the config file.

http://www.overclock.net/t/827904/how-to-multi-gpus-on-boinc

This was updated with the GTX 980Ti using limited results of one task type only (subsequent observations show that performance of different cards varies by task type; with some jobs scaling better than others):

Performance GPU Power GPUGrid Performance/Watt
211% GTX Titan Z (both GPUs) 375W 141%
156% GTX Titan X 250W 156%
143% GTX 980Ti 250W 143%
116% GTX 690 (both GPUs) 300W 97%
114% GTX Titan Black 250W 114%
112% GTX 780Ti 250W 112%
109% GTX 980 165W 165%
100% GTX Titan 250W 100%
93% GTX 970 145W 160%
90% GTX 780 250W 90%
77% GTX 770 230W 84%
74% GTX 680 195W 95%
64% GTX 960 120W 134%
59% GTX 670 170W 87%
55% GTX 660Ti 150W 92%
53% GTX 760 130W 102%
51% GTX 660 140W 91%
47% GTX 750Ti 60W 196%
43% GTX 650TiBoost 134W 80%
37% GTX 750 55W 168%
33% GTX 650Ti 110W 75%

I try to reply at the message 41294 in the trend " NVidia GPU Card comparisons in GFLOPS peak" but I don't know how (It seems I have only the choice of doing this in private. As the intention is to do a public petition and share a little information I do a "reply" in this trend. I beg your pardon for the inconveniences.).

In some messages, like this one, Skgiven has done a comparative between different types of graphics cards (thanks for the work!).

I just upgraded from a Gigabyte GTX 660 OC to a Palit GTX 1070 Dual. With the firsts results it seems the GTX 1070 outperform the GTX 660 by 265%. If I compare with this table, the GTX 1070 seems 135% faster than the Titan and a little slower than the GTX 980Ti.

Do you think that is true?

As the configuration of my computer is not optimized and I bought a Palit because its size (I didn't know its performance) I'll appreciate very much a new comparative that include the Pascal brand.
Thank you for your enlightening posts!.

I suggest that when people populate this thread with Pascal performances they focus on long tasks and stipulate the task type, their specs and setup; especially how they are using their CPU. Ideally people would test when not using their CPU or only have 1 or 2 CPU apps running on say a quad core/8thread type setup. State if you are using multiple GPU's and what PCIE version is in use. It might be an idea to focus on the 50nm SDOERR_CASP tasks first and then look at the PABLO tasks separately, as performances appear to significantly differ for these task types.

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I suggest that when people populate this thread with Pascal performances they focus on long tasks and stipulate the task type, their specs and setup; especially how they are using their CPU. Ideally people would test when not using their CPU or only have 1 or 2 CPU apps running on say a quad core/8thread type setup. State if you are using multiple GPU's and what PCIE version is in use. It might be an idea to focus on the 50nm SDOERR_CASP tasks first and then look at the PABLO tasks separately, as performances appear to significantly differ for these task types.

Is that the idea? If not, please put a example of how to do it.
*****************************************

CPU: I-5 4570, 8 Gb; 4 cores / 4 threads; Windows 10-64bits; GPU: Palit GTX 1070 Dual, 8 Gb; Driver R375.70;


Task: 15544310; Work unit: 11940364; Long runs v9.14 (cuda80):
No others BOINC works at the same time. GPU Usage: around 77%
Name: ...PABLO_SH2TRIPEP_Y_TRI_1-0-1-RND7830_0 ;
Exec. time: 15,998.75 ; CPU time: 15,858.73; Credit: 145,800
Time per step (avg over 12500000 steps):1.279 ms; PERFORMANCE: 25921 Natoms 1.279 ns/day

Task: 15546704; Work unit: 11942322; Long runs v9.14 (cuda80):
No others BOINC works at the same time. GPU Usage: around 77%
Name: ...PABLO_SH2TRIPEP_Q_TRI_1-0-1-RND3682_0 ;
Exec. time: 15,941.08 ; CPU time: 15,810.36; Credit: 145,800
Time per step (avg over 12500000 steps):1.275 ms; PERFORMANCE:25926 Natoms 1.275 ns/day

/////////////////////////////////////////////////////////////////////////////////////////////////
Task: 15553499; Work unit: 11941970; Long runs v9.14 (cuda80):
Three Rosetta works at the same time. GPU Usage: around 77%
Name: ... PABLO_SH2TRIPEP_H_TRI_1-0-1-RND1508_1;
Exec. time: 16,137.84 ; CPU time: 15,991.84; Credit: 145,800
Time per step (avg over 12500000 steps):1.290 ms; PERFORMANCE: 25911 Natoms 1.290 ns/day

/////////////////////////////////////////////////////////////////////////////////////////////////
Task: 15552458; Work unit: 11947387; Long runs v9.14 (cuda80):
Three Rosetta works at the same time. GPU Usage: around 77%
Name: ...SDOERR_CASP22S20M_crystal_contacts_50ns_a3D_2-0-1-RND9834_0;
Exec. time: 15,512.56 ; CPU time: 15,386.86; Credit: 137,850
Time per step (avg over 12500000 steps):1.240 ms; PERFORMANCE:24496 Natoms 1.240 ns/day

That's the idea.

Just tested using, zero, one, two and three CPU cores from the on my A6-3500 tri-core AMD CPU @2.1GHz:

The 1060-3GB GPU utilization went from around 90% [Linux] to ~80%, ~60% and all the way down to ~36% while running a long PABLO_SH2 task. PCIe Bandwidth Utilization (PCIE2X16) went from ~27% to ~24% to ~19% and then to ~10% (varying from 6% to 14%).

GPU Utilization while #CPU's used from tri-core crunching CPU projects, % performance loss:

CPUs Used	%GPU Utilization	%PCIe Utilization	%Increase in runtime
0		90			27			0
1		80			24			12.5
2		60			19			50
3		36			10			250

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Two more tasks without other usage of the CPU.

CPU: I-5 4570, 8 Gb; 4 cores / 4 threads; Windows 10-64bits; GPU: Palit GTX 1070 Dual, 8 Gb; Driver R375.70;

By the way, can anybody explain me what does mean "Natoms" and "ns/day". I have found this general reference but, if it's possible, I would like a more explicit one.

The "ns/day" figure gives the rate of the simulation - the higher the better. The "Natoms" figure gives the size of the system - the greater the number of atoms, the slower the simulation, in a not-quite linear relationship

Task: 15555405; Work unit: 11949955; Long runs v9.14 (cuda80):
No others works at the same time. GPU Usage: around 78%
Name: ... SDOERR_CASP1XX_crystal_ss_contacts_50ns_a3D_2-0-1-RND8379_0
Exec. time: 15,496.20 CPU time: 15,337.38; Credit: 137,850
Time per step (avg over 12500000 steps):1.239 ms; PERFORMANCE: 24496 Natoms; 1.239 ns/day


Task: 15555324; Work unit: 11949887; Long runs v9.14 (cuda80):
No others works at the same time. GPU Usage: around 78%
Name: ... SDOERR_CASP1XX_crystal_ss_contacts_50ns_a3D_0-0-1-RND5885_0
Exec. time: 15,417.61 CPU time: 15,275.11 Credit: 137,850
Time per step (avg over 12500000 steps): 1.233 ms; PERFORMANCE: 24496 Natoms; 1.233 ns/day

OT. "Natoms" and "ns/day".

Number of Atoms: number of atoms in the model (in silico molecular simulation).
Nano Seconds per day: time the model (moving proteins & other molecules) are observed.

Obviously molecules move fast; molecular reactions tend to take place between pico and micro seconds.
It's also usually the case that the researcher's name (eg. Pablo) is included in the work units name as well as some reference to the molecule or molecular region being studied (eg. SH2 is a domain/region of a protein).

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Task: 15544310; Work unit: 11940364; Long runs v9.14 (cuda80):
No others BOINC works at the same time. GPU Usage: around 77%
Name: ...PABLO_SH2TRIPEP_Y_TRI_1-0-1-RND7830_0 ;
Exec. time: 15,998.75 ; CPU time: 15,858.73; Credit: 145,800
Time per step (avg over 12500000 steps):1.279 ms; PERFORMANCE: 25921 Natoms 1.279 ns/day

Number of Atoms: number of atoms in the model (in silico molecular simulation).
Nano Seconds per day: time the model (moving proteins & other molecules) are observed.

Obviously molecules move fast; molecular reactions tend to take place between pico and micro seconds.
It's also usually the case that the researcher's name (eg. Pablo) is included in the work units name as well as some reference to the molecule or molecular region being studied (eg. SH2 is a domain/region of a protein).

FYI: in the Maxwell now thread while I compared cards ns/day Matt said:

Actually, there's a bug there.

The time reported isn't the daily rate, but the iteration time. That's inversely proportional to the daily rate, so use 1000/[value] instead.

Just tested using, zero, one, two and three CPU cores from the on my A6-3500 tri-core AMD CPU @2.1GHz:

The 1060-3GB GPU utilization went from around 90% [Linux] to ~80%, ~60% and all the way down to ~36% while running a long PABLO_SH2 task. PCIe Bandwidth Utilization (PCIE2X16) went from ~27% to ~24% to ~19% and then to ~10% (varying from 6% to 14%).

That's a big time performance loss - I'd only imagine how much a multi GPU would struggle when CPU compute is operational on this generation. Hopefully Zen rectify any bottlenecks. (I'll get a Zen if includes more PCIe lanes than Intel X99 platform. I want to build another 6 GPU Win8.1 system if I can get Nvidia drivers to cooperate. Lately the r370 driver and r375 won't allow more than 4 GPU's to run. Crunchers PM me if want to buy golden clocked GTX 970's that are stable 1.5GHz here. A mixed Pascal and Maxwell system on GPUGRID platform won't work from the app being of different CUDA generations 6.5 vs. 8.0. I hope there a fix on the chopping block.)