I'm seeing a bunch of checksum errors during unzip, anyone else have this problem?

https://www.gpugrid.net/results.php?hostid=617834&offset=0&show_names=0&state=5&appid=

Stderr output
<core_client_version>7.20.5</core_client_version>
<![CDATA[
<message>
process exited with code 1 (0x1, -255)</message>
<stderr_txt>
11:26:18 (177385): wrapper (7.7.26016): starting
lib/libcufft.so.10.9.0.58  bad CRC e458474a  (should be 0a867ac2)
boinc_unzip() error: 2

</stderr_txt>
]]>

The workunits seem to all run fine on a subsequent host.

I didn't find any of these in the 10GB 3080 errors that occurred so far today. Will check the 3070 cards shortly.

Skip

8GB 3070 card errors today checked were all:

CUDA Error of GINTint2e_jk_kernel: out of memory

Skip

I'm seeing a bunch of checksum errors during unzip, anyone else have this problem?

https://www.gpugrid.net/results.php?hostid=617834&offset=0&show_names=0&state=5&appid=

Stderr output
<core_client_version>7.20.5</core_client_version>
<![CDATA[
<message>
process exited with code 1 (0x1, -255)</message>
<stderr_txt>
11:26:18 (177385): wrapper (7.7.26016): starting
lib/libcufft.so.10.9.0.58  bad CRC e458474a  (should be 0a867ac2)
boinc_unzip() error: 2

</stderr_txt>
]]>

The workunits seem to all run fine on a subsequent host.

I didn't find any of these in the 10GB 3080 errors that occurred so far today. Will check the 3070 cards shortly.

Skip

8GB 3070 card errors today checked were all:

CUDA Error of GINTint2e_jk_kernel: out of memory

Skip

between your systems and mine, looking at the error rates;

~23% of tasks need more than 8GB
~17% of tasks need more than 10GB
~4% of tasks need more than 12GB
<1% of tasks need more than 16GB

me personally, i wouldn't run these (as they are now) with less than 12GB VRAM.

Thanx for info. As is right now the only cards I have w/ 16GB are my RX6800/6800xt cards.

https://ibb.co/hKZtR0q

Guess I need to start a go-fund-me for some $600 12GB 4070 Super cards that I've been eyeing up ;-)

Skip

Guess I need to start a go-fund-me for some $600 12GB 4070 Super cards that I've been eyeing up ;-)

Skip

a $600 12GB Titan V is like 4x faster though.

other projects are a consideration of course.

---

If this quantum chemistry project is going to last for more than a year, perhaps a $170 (via ebay) investment on Tesla P100 16G may be worth it? If you look at my gpugrid output via boincstat, I'm doing like 20M PPD over the past 4 days running on a single card with power limit of 130W. I've processed more than 1000 tasks and I think I have 2 failures with its 16G memory.

The only drawback is that there aren't many projects that do benefit from high FP64 and/or memory bandwidth performance. Originally bought it for MilkyWay. However if you have extra cash, the Titan V is a great option for such projects.

The project admin can change the granted credit and/or the task run time but as long as the high FP64 and memory bandwidth requirement remains unchanged, relatively P100 should perform better than most consumer cards for such applications.

My DCF is set to 0.02

So that is not considered zero by BOINC apparently.

Guess I need to start a go-fund-me for some $600 12GB 4070 Super cards that I've been eyeing up ;-)

Skip

a $600 12GB Titan V is like 4x faster though.

other projects are a consideration of course.

Can you point me to someplace I can educate myself a bit on using Titan V cards for BOINC. I see some for $600 used on ebay. As u know there is no used market for used 'Super' cards yet. Did u mean 4x faster than a 4070 Super or than the 3070 I would replace with it?

Thanx, Skip

Guess I need to start a go-fund-me for some $600 12GB 4070 Super cards that I've been eyeing up ;-)

Skip

a $600 12GB Titan V is like 4x faster though.

other projects are a consideration of course.

Can you point me to someplace I can educate myself a bit on using Titan V cards for BOINC. I see some for $600 used on ebay. As u know there is no used market for used 'Super' cards yet. Did u mean 4x faster than a 4070 Super or than the 3070 I would replace with it?

Thanx, Skip

Ah, it's an FP64 thing. Any other projects doing heavy FP64 lifting since the demise of MW GPU WUs?

ATMbeta tasks here have some small element of FP64. (integration)

BRP7 tasks at Einstein also use FP64 a little bit.

Asteroids@home GPU apps are also primarily FP64, but they have massive GPU memory bandwidth bottleneck that slows things down more than the FP64 does anyway so you don't realize the benefit there. and the CPUs are better production per watt at Asteroids.

not sure if any any other projects use it.

---

between your systems and mine, looking at the error rates;

~23% of tasks need more than 8GB
~17% of tasks need more than 10GB
~4% of tasks need more than 12GB
<1% of tasks need more than 16GB

me personally, i wouldn't run these (as they are now) with less than 12GB VRAM.

Not sure why but...

Error rates seemed to start dropping after 5pm (23:00 Zulu) today. Overall error average since 2/11 across my 5 Nvid cards was 26.7% with it slowly creeping down over time. Early on a little bit of this was the result of lowering clocks to eliminate the occasional segfault (0x8b).

The average of the last two captures today across the 5 cards was 20.5%

For the last 6 hour period I just checked, my 10GB card average error rate dropped to 17.3% (15.92 & 18.7) and the 8GB card error rate was at 21.3%.

Skip

les unites de calcul pour windows sont elles arrivées?


Have the computing units for windows arrived?

---

It isn't the tasks which need to be released, it's the application programs needed to run them.

You can read the list of applications at https://www.gpugrid.net/apps.php

The newest ones tend to be towards the bottom of the page - and no, there isn't one for 'Quantum chemistry calculations on GPU' yet.

Bookmark that page - there isn't a direct link to it on this site, although it's a standard feature of BOINC projects.

Watching Stderr output report for a certain PYSCFbeta task, can be found a line like this:

.
+ CUDA_VISIBLE_DEVICES=N
.

Where "N" corresponds to the Device Number (GPU) where the task was run on.
This is very much appreciated on multi GPU hosts when trying to identify reliable or unreliable devices.
This allows, if desired, to exclude unreliable devices as of this Ian&Steve C. kind advice.

A similar feature would be useful at other apps, as ATMbeta.

between your systems and mine, looking at the error rates;

~23% of tasks need more than 8GB
~17% of tasks need more than 10GB
~4% of tasks need more than 12GB
<1% of tasks need more than 16GB

me personally, i wouldn't run these (as they are now) with less than 12GB VRAM.

Not sure why but...

Error rates seemed to start dropping after 5pm (23:00 Zulu) today. Overall error average since 2/11 across my 5 Nvid cards was 26.7% with it slowly creeping down over time. Early on a little bit of this was the result of lowering clocks to eliminate the occasional segfault (0x8b).

The average of the last two captures today across the 5 cards was 20.5%

For the last 6 hour period I just checked, my 10GB card average error rate dropped to 17.3% (15.92 & 18.7) and the 8GB card error rate was at 21.3%.

Skip

IGNORE... all went to crap the next day (today)

yeah i've been seeing higher error rates on my 12GB cards too.

still very low on my 16GB cards though.

---

My preferences are set to receive work from all apps, including beta ones, but none of my 4 GB VRAM graphics cards have received lately PYSCFbeta tasks.
Casual, or scheduler-driven behavior?
In the meanwhile, they are performing ATMbeta tasks without a single processing error so far.
And unsent PYSCFbeta tasks seem to be growing more and more, 39K+ at this moment.

My GPUs are all on the smaller-memory side, too. Since ATMbeta tasks became available again, I haven't picked up a single Quantum chemistry task.

I think it's either a cunning project plan, or (more likely) some subtle BOINC behaviour concerning our hosts' "reliability" rating on particular task types.

My GPUs are all on the smaller-memory side, too. Since ATMbeta tasks became available again, I haven't picked up a single Quantum chemistry task.

I think it's either a cunning project plan, or (more likely) some subtle BOINC behaviour concerning our hosts' "reliability" rating on particular task types.

it's because you have test tasks enabled. with that, it's giving preferential treatment for ATM tasks which are classified in the scheduler as beta/test.

QChem seems to not be classified in the scheduler as "test" or beta. despite being treated as such by the staff and the app name literally has the word beta in it. if you disable test tasks, and enable only QChem, you will get them still.

---

it's because you have test tasks enabled. with that, it's giving preferential treatment for ATM tasks which are classified in the scheduler as beta/test.

Thank you, that fully explains the fact.
In the dilemma of choosing between my 50% erroring PYSCFbeta or 100% succeeding ATMbeta tasks, I'll keep this last.

bonjour,
j'aimerais calculer pour atmbeta avec ma gtx 1650 et pour quantum chemistry avec ma rtx 4060.
Je ne parviens pas a modifier le config.xml pour cela.
Je n'ai que des unités atmbeta a calculer et aucune unités quantum chemistry.
voici ce que j'ai mis dans le fichier config.xml de boinc.
Quelqu'un pourrait il m'aider.Merci d'avance.

Good afternoon,
I would like to calculate for atmbeta with my gtx 1650 and for quantum chemistry with my rtx 4060.
I can’t change the config.xml for this.
I only have atmbeta units to calculate and no quantum chemistry units.
here is what I put in the config.xml file of boinc.
Someone could help me. Thanks in advance


<cc_config>
<options>
<exclude_gpu>
<url>https://www.gpugrid.net/</url>
[<device_num>0</device_num>]
[<type>NVIDIA</type>]
[<app>ATMbeta</app>]
</exclude_gpu>
<exclude_gpu>
<url>https://www.gpugrid.net/</url>
[<device_num>1</device_num>]
[<type>NVIDIA</type>]
[<app>PYSCFbeta</app>]
</exclude_gpu>
<exclude_gpu>
<url>http://asteroidsathome.net/boinc/</url>
<device_num>0</device_num>
<type>NVIDIA</type>
</exclude_gpu>
<exclude_gpu>
<url>https://einstein.phys.uwm.edu/</url>
<device_num>0</device_num>
<type>NVIDIA</type>
</exclude_gpu>
<use_all_gpus>1</use_all_gpus>
<ncpus>-1</ncpus>
</options>
</cc_config>

---