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Gorgon

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  • Location
    Great White North
  • Occupation
    Retired Geek
  • Member title
    Folding Hard or Hardly Folding

System

  • CPU
    AMD Ryzen 9 7950x
  • Motherboard
    Asus X670e ProArt WiFi
  • RAM
    4 x 32GB DDR5-5600
  • GPU
    AMD RadeonPro W4100; MSI RTX 4070Ti Super
  • Case
    Lian-Li O11 Air Mini
  • Storage
    2x500GB Samsung 980 PCIe3 NVMe (OS); 2 x 8TB Seagate NAS (Data)
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    Corsair SF750
  • Display(s)
    3 Asus ProArt 24” 1080p
  • Cooling
    EVGA 240mm CLC
  • Keyboard
    Corsair Gaming
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    MS Bluetooth
  • Sound
    On-Board (Realtek ALC1220VB); Edifier R1280T
  • Operating System
    Windows 10 Pro
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  1. I've been taking a look at it a little deeper by diving into my HfM data and it turns out the current crop of poorly yielding WUs are all ones with Small (<100,000) Atom sizes and most of these are Categorized as "Unspecified." It just turns out that a few Projects have launched that need these smaller Models. I'm just going to leave things stock (Preference = "Any") as it's work worth doing but setting a Preference for Alzheimer's or Cancer would likely yield better on Current Mid to High-Tier GPUs (i.e. >=4070) as most of these WUs have larger Atom sizes.
  2. Have you set your cause to Alzheimer's or something else? I'm still seeing lower aggregate PPD these days from about 125-127Mppd down to 118-123MPPD but I'm currently just running "Any" for my Cause preference. There seem to be a glut of poorly yielding p12296-7 & p14951 "Unspecified" projects currently.
  3. Nope, not seeing a 14% increase in Production - if anything I'm seeing around a 2.5% decrease over the last week. I'm guessing you picked up a few "Unicorn" WUs
  4. A belated Welcome to Team LTT then! For future reference the easiest way is to just click on your Folding Legend Badge here which is linked to your EoC Stats page Perhaps you had to change your username in the past or get a new PassKey which, I believe, would give you a different ID on the EoC Stats page. Here is your Stats Page at F@H which shows the other Teams you've folded on using your current username but, unfortunately, does not show the start date. I believe Jason@EoC also prunes the members from Teams if they've been inactive for a while to keep things a bit more manageable.
  5. I assUme you are referring to Linux? Running Folding at Home or any other intensive GPU Load will test the stability of the system. For CPU testing I usually use mprime -twhich is the *nix variant of a Prime 95 Samll FFT Torture Test. Editing Errors???
  6. The server may be reachable but it may be out of disk space or the collection process has hung - typical long weekend stuff - likely have to wait until Tuesday for it to be fixed - check the server status
  7. RIP. Wow - That is an expensive lesson. Glad your back up and running.
  8. Then it likely you either have your numbers backwards or you are comparing Apples to Oranges (i.e. NOT the same Work Units). It is unlikely a Quadro RTX 6000 with 4608 Turing CUDA Cores is double the performance of a RTX 3080 with 8704 Ampere Cores. The LAR Database has it at 3.5 versus 4.8MPPD for the 3080 and 16.31 versus 29.77 TFLOPS (FP32). Without knowing which Projects (Work Units) were running on each card when you observed this it's difficult to say what the cause might be but it is likely that you observed the 3080 running a poorly performing Work Unit while the RTX 6000 was running a WU that performed adequately or above average. If you want to do a meaningful comparison then you'd have to use a tool like Harlam's Folding Monitor (HfM.net) to monitor the GPUs for a several days then compare the performance of the two cards running the same Projects and discounting projects with small (<50,000) Atom Sizes. For example, one of my 3080s, running between January 1st and the end of February, when it was decommissioned and replaced with a 4070 Ti Super, had PPDs ranging from 4.3 (p12446) and 7.7MPPD (p12264) Clock-Limited to 1440MHz so a 3080 running at Stock should be 15-20% more performant.
  9. Honestly, as others have said if you don't have a 4-head VCR with S-Video output then you'll likely spend less money using a Service to digitize the content. Old VHS tapes can be tricky as depending on what they were recorded on there can be tracking issues (picture losing Sync and rolling or jittering up and down a bit). The video information is stored on tape as a Luminance signal and a Color (Chroma) signals. The best quality transfer will be using a S-Video rather than Composite (Yellow RCA Connector). Video Equipment is loosely divided into Consumer, Industrial and Broadcast equipment in increasing order of Quality and exponential increases in price. Good transfer houses typically use Industrial VHS VCR with Time Base Correctors. If you have a decent VCR and want to do the transfers yourself I'd recommend looking for a Capture Device that has a S-Video input and includes software that can save in MPG or MKV formats to save time not having to transcode from intermediate formats such as DVI. These shouldn't be that expensive these days. I can't make any specific recommendations as I transferred all my VHS footage years ago using a JVC D-VHS machine and a Firewire DVI Capture box. Hint. Before transferring any Cassette you might want to Fast-Forward then Rewind the Cassette first to un-pack the tape. See This Link Also many Public Library have Digitization Stations that have VHS to DVD Decks.
  10. See the BOINC Projects Summary. There's Fight AIDS at Home on World Community Grid but a lot of these projects are intermittent.
  11. Folding at Home has been used for Research into AIDS/HIV in the past. It's really up to the areas the Researchers are currently interested in.
  12. An A6000 (Ada) should Yield about 12.9MPPD on average versus 4.8MPPD for a 3080. Not too surprising given the 10,752 versus the 8,704 Shader Cores, the Quick Return Bonus (QRB) and having similar Base Clocks and being of the same generation. As for VRAM, Folding at Home only uses 1-2GB of VRAM when running and most cards see little to no decrease in performance running in PCIe slots at x8 versus x16. Currently F@H only leverages the Shader Cores so Tensor and RT Cores have no impact. Typically the FP32 performance of a card within the same generation (or at least since NVIDIA changed their definition of this in Turing) will sort of (due to the QRB) directly translate to the performance difference. Better FP64 performance will make a lesser impact in some work units (WUs) but both AMD and NVIDIA have "nerfed" their FP64 performance in recent generations.
  13. Spent the last week rebuilding my Daily Driver.

     

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