3 ms·

Well, DRAM still has 2+ orders of magnitude lower latency than SSDs. Whether that matters clearly depends on your application. On a desktop system switching ta

Well, DRAM still has 2+ orders of magnitude lower latency than SSDs. Whether that matters clearly depends on your application. On a desktop system switching tabs in your browser, swapping the content in from SSD will be fine. A ML application on a server accessing memory quasi randomly won't.

dogma1138 6y ago
Using SSDs for ML is what vendors like NVIDIA and AMD are trying to solve because this is a fallacy as you will never have as much RAM as you want / need compared to the size of your datasets. https://developer.nvidia.com/blog/gpudirect-storage/ https://developer.nvidia.com/blog/gpudirect-storage/ It’s the same fallacy as RAM size impacts video editing, it doesn’t. 8K RED raw is 4.374 terabytes per hour... at that point it doesn’t matter if you have 8, 16 or even 256GB of RAM there if your storage is too slow to support it you’ll drop frames and or have huge seek times, more memory won’t really help you there is no amount of prefetch you can do to bridge gaps that big. There is a huge advantage of having a unified memory architecture where the application can address a single memory address space to access your data and you do not need to perform memory copies, allocations and all that stuff between your storage, CPU and GPU...
- nix23 6y ago
  >It’s the same fallacy as RAM size impacts video editing, it doesn’t. Computers are used for much more then just Video Editing...for Fluid Simulation Ram definitely has a impact. And Talking about SSD (QLC)...if they run out of SLC-Cache they are sometimes slower then HDD's. And i bet your Video editing is faster if you have 4TB of RAM.
- zaphirplane 6y ago
  Wouldn’t having more ram give the process more runway to prefetch data into ram before it’s needed ? Without having new prefetch evicting unprocessed older prefetched data
  - dogma1138 6y ago
    Not really you’ll run out of your buffer regardless of its size if it would be slower to fill it than to go through it. It also doesn’t help you at all for random access.

dogma1138 6y ago

Using SSDs for ML is what vendors like NVIDIA and AMD are trying to solve because this is a fallacy as you will never have as much RAM as you want / need compared to the size of your datasets. https://developer.nvidia.com/blog/gpudirect-storage/ https://developer.nvidia.com/blog/gpudirect-storage/ It’s the same fallacy as RAM size impacts video editing, it doesn’t. 8K RED raw is 4.374 terabytes per hour... at that point it doesn’t matter if you have 8, 16 or even 256GB of RAM there if your storage is too slow to support it you’ll drop frames and or have huge seek times, more memory won’t really help you there is no amount of prefetch you can do to bridge gaps that big. There is a huge advantage of having a unified memory architecture where the application can address a single memory address space to access your data and you do not need to perform memory copies, allocations and all that stuff between your storage, CPU and GPU...

nix23 6y ago
>It’s the same fallacy as RAM size impacts video editing, it doesn’t. Computers are used for much more then just Video Editing...for Fluid Simulation Ram definitely has a impact. And Talking about SSD (QLC)...if they run out of SLC-Cache they are sometimes slower then HDD's. And i bet your Video editing is faster if you have 4TB of RAM.
zaphirplane 6y ago
Wouldn’t having more ram give the process more runway to prefetch data into ram before it’s needed ? Without having new prefetch evicting unprocessed older prefetched data
- dogma1138 6y ago
  Not really you’ll run out of your buffer regardless of its size if it would be slower to fill it than to go through it. It also doesn’t help you at all for random access.