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What are the use cases for the Titan V vs. Radeon Pro SSG? Just ML? Higher memory bandwidth? https://pro.radeon.com/en/product/pro-series/radeon-pro-ssg/ http

by alvern 9y ago

What are the use cases for the Titan V vs. Radeon Pro SSG? Just ML? Higher memory bandwidth? https://pro.radeon.com/en/product/pro-series/radeon-pro-ssg/ https://pro.radeon.com/en/product/pro-series/radeon-pro-ssg/

vosper 9y ago
That Radeon card has 2TB of onboard memory, and seems to be aimed at very heavy content creation workloads (8k VR). I think it's in a different (and ultra-niche) league to this Nvidia card. I would guess this is aimed at heavy ML workloads?
freeone3000 9y ago
It's nvidia. You get CUDA and CuDNN, which means the standard suite of ML programs. AMD has no equivalent offering.
- lovelearning 9y ago
  ROCm and MIOpen are AMD's equivalent offerings. They've developed their own ports for some of the popular DL frameworks. https://rocm.github.io/dl.html https://rocm.github.io/dl.html
  - aseipp 9y ago
    Very few people who do DL professionally or as casual hobbyists have the time to sort out ports of various frameworks to work with in-progress compute drivers/toolchain. Most engineers aren't cash strapped, they'll just buy what they need instead of waste time, especially when the field and the tools move so quickly. It's just not important enough when NVidia cards work well out of the box and can be acquired at both the hobbyist low end (GTX 1060) and professional high end (Tesla series), have proven track records, and every DL framework works more-or-less immediately. They need the patches to be upstream and widely maintained by the developers (and be involved themselves) before people 'at large' will really consider using them. The code is there, but practically doesn't exist for anyone yet, outside of AMD developers and a minuscule amount of AMD users. This is also true of all of Intel's ports of various DL frameworks, too. One thing NVidia has learned is that sticking their engineers directly into the pipeline works -- from everything from game development studios, and now to deep learning frameworks (which have nvidia engineers). That said, AMD cards do have some excellent advantages like unlocked FP16 that Nvidia does not offer outside of the Tesla series, and bringing card price down would rock. So I'm hopeful that they'll sort all this out and make themselves competitive. I'm hoping to see MIOpen support (and if we want ponies, an upstream ROCm driver) sometime in the next ~year for at least a few frameworks (Caffe2 or PyTorch would be good.) But until then, CUDA, CuDNN and Nvidia technology are really the only name in town for like +90% of people, for better or worse.
    - andars 9y ago
      > Unlocked FP16 ... outside of the Tesla series Titan V has HMMA, so it most likely also has full hardware FP16. It seems to have a binned V100 chip with 3 functioning HBM2 stacks.
krautsourced 9y ago
GPU rendering. It's pretty much all CUDA, with very few exceptions.

by alvern 9y ago

vosper 9y ago

That Radeon card has 2TB of onboard memory, and seems to be aimed at very heavy content creation workloads (8k VR). I think it's in a different (and ultra-niche) league to this Nvidia card. I would guess this is aimed at heavy ML workloads?

freeone3000 9y ago

It's nvidia. You get CUDA and CuDNN, which means the standard suite of ML programs. AMD has no equivalent offering.

lovelearning 9y ago
ROCm and MIOpen are AMD's equivalent offerings. They've developed their own ports for some of the popular DL frameworks. https://rocm.github.io/dl.html https://rocm.github.io/dl.html
- aseipp 9y ago
  Very few people who do DL professionally or as casual hobbyists have the time to sort out ports of various frameworks to work with in-progress compute drivers/toolchain. Most engineers aren't cash strapped, they'll just buy what they need instead of waste time, especially when the field and the tools move so quickly. It's just not important enough when NVidia cards work well out of the box and can be acquired at both the hobbyist low end (GTX 1060) and professional high end (Tesla series), have proven track records, and every DL framework works more-or-less immediately. They need the patches to be upstream and widely maintained by the developers (and be involved themselves) before people 'at large' will really consider using them. The code is there, but practically doesn't exist for anyone yet, outside of AMD developers and a minuscule amount of AMD users. This is also true of all of Intel's ports of various DL frameworks, too. One thing NVidia has learned is that sticking their engineers directly into the pipeline works -- from everything from game development studios, and now to deep learning frameworks (which have nvidia engineers). That said, AMD cards do have some excellent advantages like unlocked FP16 that Nvidia does not offer outside of the Tesla series, and bringing card price down would rock. So I'm hopeful that they'll sort all this out and make themselves competitive. I'm hoping to see MIOpen support (and if we want ponies, an upstream ROCm driver) sometime in the next ~year for at least a few frameworks (Caffe2 or PyTorch would be good.) But until then, CUDA, CuDNN and Nvidia technology are really the only name in town for like +90% of people, for better or worse.
  - andars 9y ago
    > Unlocked FP16 ... outside of the Tesla series Titan V has HMMA, so it most likely also has full hardware FP16. It seems to have a binned V100 chip with 3 functioning HBM2 stacks.

krautsourced 9y ago

GPU rendering. It's pretty much all CUDA, with very few exceptions.