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Why does folding@home assign weak gpus work units they cannot hope to compelte in time?

I have a secondary r5 240 that I use as a vga output. I have it run as a gpu in folding@home, and it gets very large work units that take days to complete, and rarely complete in time because I turn my computer off at night.

 

Below is the GPU trying in vain to complete a unit that it got assigned barely making it 40% through the previous unit.

image.png.9a0c4756cd7c474ee3dab7d5512939e7.pngimage.png.569b716eee001b02c3a038821864940e.png

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21 minutes ago, gamagama69 said:

I have a secondary r5 240 that I use as a vga output. I have it run as a gpu in folding@home, and it gets very large work units that take days to complete, and rarely complete in time because I turn my computer off at night.

 

Below is the GPU trying in vain to complete a unit that it got assigned barely making it 40% through the previous unit.

 

Because it expects you to leave it running. Folding@home is supposed to be something you turn on when you aren't using your computer. Basically you are donating your money via electricity bill. 

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6 minutes ago, DANK_AS_gay said:

Because it expects you to leave it running. Folding@home is supposed to be something you turn on when you aren't using your computer. Basically you are donating your money via electricity bill. 

yeah but 12 days is longer than the expiration period of the unit

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27 minutes ago, gamagama69 said:

yeah but 12 days is longer than the expiration period of the unit

use a faster gpu then

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Likely there simply aren't any WUs that are sized for such a slow GPU anymore. 

F@H
Desktop: i9-13900K, ASUS Z790-E, 64GB DDR5-6000 CL36, RTX3080, 2TB MP600 Pro XT, 2TB SX8200Pro, 2x16TB Ironwolf RAID0, Corsair HX1200, Antec Vortex 360 AIO, Thermaltake Versa H25 TG, Samsung 4K curved 49" TV, 23" secondary, Mountain Everest Max

Mobile SFF rig: i9-9900K, Noctua NH-L9i, Asrock Z390 Phantom ITX-AC, 32GB, GTX1070, 2x1TB SX8200Pro RAID0, 2x5TB 2.5" HDD RAID0, Athena 500W Flex (Noctua fan), Custom 4.7l 3D printed case

 

Asus Zenbook UM325UA, Ryzen 7 5700u, 16GB, 1TB, OLED

 

GPD Win 2

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1 hour ago, gamagama69 said:

I have a secondary r5 240 that I use as a vga output. I have it run as a gpu in folding@home, and it gets very large work units that take days to complete, and rarely complete in time because I turn my computer off at night.

 

Below is the GPU trying in vain to complete a unit that it got assigned barely making it 40% through the previous unit.

image.png.9a0c4756cd7c474ee3dab7d5512939e7.pngimage.png.569b716eee001b02c3a038821864940e.png

Project 18213 is an Alzheimer's WU with 302,000 Atoms and a 2-day time-out and 5 day completion deadline. So yes, a very large WU which should not be getting assigned to an HD8570 with 384 cores.

 

The servers are supposed to divide the clients into bands for very fast, fast & slow GPUs and assign appropriate WUs based on the capabilities of the card but this has some limitations.

 

Take a look at the projects list and look for low atom count OpenMM_22 (GPU) projects with small atom counts and see if a particular "cause" is running those jobs then you could try changing your preferences in the client to influence getting smaller jobs.

 

Realistically, as others have pointed out, there's not a lot you can likely contribute to Folding at Home with a almost 10 year old GPU. If you want to contribute something then you might be able to find a BOINC Project as these tend to have better support for older hardware. Folding at Home tends to lean towards more modern, faster GPUs.

FaH BOINC HfM

Bifrost - 6 GPU Folding Rig  Linux Folding HOWTO Folding Remote Access Folding GPU Profiling ToU Scheduling UPS

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1 hour ago, emosun said:

use a faster gpu then

I mean I'm using this as a basic video output card in addition to my main 3080, which chews through larger units in a few hours with not even consistent 100% usage.

 

I just don't get why they don't assign smaller work units to weaker cards.

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16 minutes ago, gamagama69 said:

I just don't get why they don't assign smaller work units to weaker cards.

They do in principle, but that GPU's probably outside of any considerations they might have.

 

F@H sure is built on "every little bit counts" but beyond a certain point there are considerations that make it worthless. Energy efficiency for example, your r5 is going to be using about 20x more energy than your 3080 to do the same job. 

F@H
Desktop: i9-13900K, ASUS Z790-E, 64GB DDR5-6000 CL36, RTX3080, 2TB MP600 Pro XT, 2TB SX8200Pro, 2x16TB Ironwolf RAID0, Corsair HX1200, Antec Vortex 360 AIO, Thermaltake Versa H25 TG, Samsung 4K curved 49" TV, 23" secondary, Mountain Everest Max

Mobile SFF rig: i9-9900K, Noctua NH-L9i, Asrock Z390 Phantom ITX-AC, 32GB, GTX1070, 2x1TB SX8200Pro RAID0, 2x5TB 2.5" HDD RAID0, Athena 500W Flex (Noctua fan), Custom 4.7l 3D printed case

 

Asus Zenbook UM325UA, Ryzen 7 5700u, 16GB, 1TB, OLED

 

GPD Win 2

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2 minutes ago, Kilrah said:

They do in principle, but that GPU's probably outside of any considerations they might have.

 

F@H sure is built on "every little bit counts" but beyond a certain point there are considerations that make it worthless. Energy efficiency for example, your r5 is going to be using about 20x more energy than your 3080 to do the same job. 

that's true. I've tried to oc it before but msi afterburner doesn't allow it for some reason, it might be because it's an oem card

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It's a good thing electronics are non-sentient, that poor little 240 would be having an emotional breakdown curled up crying in the corner.

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2 hours ago, BiotechBen said:

It's a good thing electronics are non-sentient, that poor little 240 would be having an emotional breakdown curled up crying in the corner.

please stop he's trying his best I believe in him

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On 4/13/2022 at 2:10 PM, LavenderPhantom78139 said:

I have several r5 250s i complete work units with. Idk what the performance gap between the 250 and 240 is

according to tpu the difference is like 40%.

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