Ian&Steve C. suggested a comparison between Microsoft and Linux hosts (https://www.gpugrid.net/forum_thread.php?id=5194&nowrap=true#55895)

An interesting thought, so data has been captured and summarized below showing Average Credit per Day for Linux and Microsoft Hosts for each GPU model.

The results are very subjective as the hosts are not in a controlled environment.

There is no control over:

Thermal environment,
Host and PCIe specifications,
CPU/GPU utilisation for other projects,
Power limiting or Overclocking,
Software / OS configurations.

The lack of a controlled environment makes this comparison questionable, but I was still curious, so data is presented out of interest rather than being an exact comparison.

Make your own conclusions and feel free to comment.

I wonder why the average 2080ti isnt scoring better. I have 6x 2080tis and have seen 2080tis in other hosts also scoring north of 1,000,000 per day per card under Linux.

how are you calculating average credit? is it based on individual task run times? or did you just add up all the credit earned from that day for each host then average them? If the latter, then maybe some hosts weren't contributing the entire day.
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I wonder why the average 2080ti isnt scoring better. I have 6x 2080tis and have seen 2080tis in other hosts also scoring north of 1,000,000 per day per card under Linux.

how are you calculating average credit? is it based on individual task run times? or did you just add up all the credit earned from that day for each host then average them? If the latter, then maybe some hosts weren't contributing the entire day.

yeah i think that would be more appropriate to remove that extra variable. MDAD only i think is a good idea. see what happens
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For each GPU model:
Average Credit / Day = (86400 / Total Runtime) * Total Credit

re you applying this formula to every task individually? and then averaging the results? for each host?

because even within the MDAD group on the same host/GPU, there seems to be varying credit reward per unit time.

take the following two tasks results as examples. both from my 2080ti system above. both MDAD tasks. wildly different calculated credit/day.

From the dataset used, I have grabbed 443 tasks from your host 546526.
Using the above calculation, the Average Credit / Day is 1,037,108.
This lines up with the RAC of 1,042,210 for this host. The calculation I use appears ok.

can you elaborate on what you mean by total run time and credit for each gpu model? do you mean to say you take the sum of all the runtimes and credits of the thousands of tasks across all the hosts and do the calculation in a single equation?

it just seems odd for so many hosts to perform so poorly. I'm not doing anything "special" with mine. I've even power limited them, reducing their production slightly to increase power efficiency.

I'm trying to think if your method is somehow being skewed by the individual hosts behavior (like someone who's not running their host 24/7, or not running GPUGRID full time and sharing resources with other projects). but assuming you're basing your numbers on only the tasks they submit, and they aren't running more than 1 WU at a time, this shouldnt be the case. or perhaps a lot of other hosts are getting a disproportionate amount of low credit tasks? but again I would expect the task distribution to be more or less the same for everyone.

I have no other explanation for it though.
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can you elaborate on what you mean by total run time and credit for each gpu model? do you mean to say you take the sum of all the runtimes and credits of the thousands of tasks across all the hosts and do the calculation in a single equation?

Thank you for putting this information together. Always knew that there was Linux had an advantage over Windows but would have never suspected to see this big of a difference in performance.

Thanks for the detailed analysis. Much appreciated

Don't forget to do at least bare minimum of statistical computations. This exercise was not of much use. (Arithmetic avg on avg is wrong - I think it should be harmonic, were are other basic characterizations like variance?)

Depending on those calculations, that ~20% "advantage" might still be insignificant.

I think ANOVA would be far better method. (Assuming data fulfill assumptions of method, otherwise one of non-parametric comaprative tests would have to be used)

Don't forget to do at least bare minimum of statistical computations. This exercise was not of much use. (Arithmetic avg on avg is wrong - I think it should be harmonic, were are other basic characterizations like variance?)

Depending on those calculations, that ~20% "advantage" might still be insignificant.

I think ANOVA would be far better method. (Assuming data fulfill assumptions of method, otherwise one of non-parametric comaprative tests would have to be used)

Arithmetic avg on avg is wrong

that ~20% "advantage" might still be insignificant

Linux has a performance advantage over Windows (Vista and newer) operating systems due their different technological approach.
After releasing Windows XP (and Server 2003 series) Microsoft decided to sacrifice some GPU performance to make their OS more stable: whenever an exception takes place in the device driver, it won't take down the whole OS (= there's no Blue Screen Of Death).
They refined this technology in the later editions of Windows 10, but the latency is still there.
See my post about it with links.
Linux does not have this feature (as far as I know), so there's less latency in Linux in every CPU-GPU interaction. The impact of this on the overall performance of a given GPUGrid workunit greatly depends on how much (how many per second) CPU intervention is needed to run the given simulation. The more intervention the bigger the gain on Linux over Windows. So you have to redo your statistics for every different simulation.

Linux has a performance advantage over Windows (Vista and newer) operating systems due their different technological approach.
After releasing Windows XP (and Server 2003 series) Microsoft decided to sacrifice some GPU performance to make their OS more stable: whenever an exception takes place in the device driver, it won't take down the whole OS (= there's no Blue Screen Of Death).
They refined this technology in the later editions of Windows 10, but the latency is still there.
See my post about it with links.
Linux does not have this feature (as far as I know), so there's less latency in Linux in every CPU-GPU interaction. The impact of this on the overall performance of a given GPUGrid workunit greatly depends on how much (how many per second) CPU intervention is needed to run the given simulation. The more intervention the bigger the gain on Linux over Windows. So you have to redo your statistics for every different simulation.