3 ms·

"With TITAN V, we are putting Volta into the hands of researchers and scientists all over the world" Screw you NVIDIA. I've been vocal about this for a while b

"With TITAN V, we are putting Volta into the hands of researchers and scientists all over the world" Screw you NVIDIA. I've been vocal about this for a while but this is now getting utterly ridiculous, and frankly insuting to the research community. I don't mind that NVIDIA is ripping of the big derplearning -- and more recently "data" -- companies (Goog, FB, Baidu, etc.), but this greedy bullshit attitude led to the Tesla cards all of a sudden going from expensive, with a 2-2.5x price jump, to the ridiculously priced. In the meantime, they've been "enabling" research by throwing peanuts at the science community with cancer stunts [1]. However, that was just the facade, the real story was that they'd found the cash-cow and the enthusiastic HPC/sci-comp crowd that helped NVIDIA grow out of their sexy-witch-on-the-front-panel-of-the-GTX company box and step into the "big compute boys' league" is no more than just another means to convert peanuts to marketing material. After years of complaints that there are no sensibly prices _compute_ cards devs and independent researchers can afford, constant battles with NVIDIA to stop crippling their management tools on GTX cards, to allow cheap cards to be used productively for compute and development, etc. they step up their game and release a $3k "affordable" card for "researchers and scientists". This claim is beyond ridiculous, revolting and, as a researcher (working in HPC on a large FOSS simulation project) this is insulting. Which researchers are they talking about, the ones employed by the big ad, finance, companies? Definitely not me, not us! Time for us to start showing NVIDIA the middle finger for good! Get porting people, support AMD, test ROCm, their openness and attitude receptive to public feedback and criticism has impressed me recently. At the same time, we need to push NVIDIA too: file bug reports against NVIDIA's shitty OpenCL support, against their consumer card crippling drivers and management tools, and resist their efforts to blackmail OEMs to stop selling servers with GeForce. [1] https://www.nextplatform.com/2016/11/15/deep-learning-supercomputer-approach-cancer-research/ https://www.nextplatform.com/2016/11/15/deep-learning-superc...

robotresearcher 9y ago
If you can't afford the $3K card, buy the $800 card. The $800 card that outperforms the fastest SIMD computer any amount of money could buy just a few years ago. NVIDIA makes fast computers and sells them for market prices. I'm not sure we need to storm their castle with pitchforks.
- slizard 9y ago
  Please show me where can you buy a $800 card that can be used to develop for the _compute_ architectures; i.e. the GV100 or the GP100 for that matter. Also, please show me how can you use those $800 cards for serious computing/research without potentially running into silly issues and a lot of pain (from not being able to use the "supported" standard OpenCL to running into cooling, fan speed, clocking issues that makes reliable perf tuning a pain, etc.).
  - andars 9y ago
    It is strange that you expect to be able to buy the server grade GPUs for GeForce series prices and are outraged when reality doesn't meet your expectations. It strikes me as pretty awesome that the V100 is now available for only $3k in the Titan V, rather than ~$10k for the Tesla V100. I reckon you need to readjust your expectations.
    - slizard 9y ago
      > It is strange that you expect to be able to buy the server grade GPUs for GeForce series prices Respectfully, you are clearly missing the point. You can buy <$500 USD Xeon Silver parts (and fairly affordable barebone workstation or servers to go with) and you'll get the Xeon-SP architecture to develop for. You can even buy the rebadged i9 parts too for ~$1000-$1200 with both FMA unit enabled, but admittedly those are also rather expensive. > and are outraged when reality doesn't meet your expectations. Untrue. I want an option, any option that allows researchers to develop for the fancy high-end chips -- especially when they claim that they want to and are providing such an option. Computational scientist and HPC researchers who aim to write code for, or porting community codes to machines like DOE Sierra, Summit or future V100-based machines need something that is within the reach of a junior PI, a researcher in countries less well off than the rich western Universities and research institutes. In contrast, jack up the price this high means that the Facebook et. al will still find it worthwhile, but the grand majority of actual researchers, those that they claim to be enabling and helping with this card will not be able to afford it. It all too easy to forget how the "democratization" of supercomputing was NVIDIA's slogan very ambitious and cool [1] that has over time turned into a marketing claim [2] that offers little in terms of bottom-up enabling the community. [1] http://gpgpu.org/static/asplos2008/ASPLOS08-1-intro-overview.pdf http://gpgpu.org/static/asplos2008/ASPLOS08-1-intro-overview... [2] http://assets.nvidia.com/nv/tesla/pdf/NVIDIA_Accelerate%20Your%20Datacenter%20with%20Tesla%20P100_Whitepaper_Dec2016.pdf http://assets.nvidia.com/nv/tesla/pdf/NVIDIA_Accelerate%20Yo...

robotresearcher 9y ago

If you can't afford the $3K card, buy the $800 card. The $800 card that outperforms the fastest SIMD computer any amount of money could buy just a few years ago. NVIDIA makes fast computers and sells them for market prices. I'm not sure we need to storm their castle with pitchforks.

slizard 9y ago
Please show me where can you buy a $800 card that can be used to develop for the _compute_ architectures; i.e. the GV100 or the GP100 for that matter. Also, please show me how can you use those $800 cards for serious computing/research without potentially running into silly issues and a lot of pain (from not being able to use the "supported" standard OpenCL to running into cooling, fan speed, clocking issues that makes reliable perf tuning a pain, etc.).
- andars 9y ago
  It is strange that you expect to be able to buy the server grade GPUs for GeForce series prices and are outraged when reality doesn't meet your expectations. It strikes me as pretty awesome that the V100 is now available for only $3k in the Titan V, rather than ~$10k for the Tesla V100. I reckon you need to readjust your expectations.
  - slizard 9y ago
    > It is strange that you expect to be able to buy the server grade GPUs for GeForce series prices Respectfully, you are clearly missing the point. You can buy <$500 USD Xeon Silver parts (and fairly affordable barebone workstation or servers to go with) and you'll get the Xeon-SP architecture to develop for. You can even buy the rebadged i9 parts too for ~$1000-$1200 with both FMA unit enabled, but admittedly those are also rather expensive. > and are outraged when reality doesn't meet your expectations. Untrue. I want an option, any option that allows researchers to develop for the fancy high-end chips -- especially when they claim that they want to and are providing such an option. Computational scientist and HPC researchers who aim to write code for, or porting community codes to machines like DOE Sierra, Summit or future V100-based machines need something that is within the reach of a junior PI, a researcher in countries less well off than the rich western Universities and research institutes. In contrast, jack up the price this high means that the Facebook et. al will still find it worthwhile, but the grand majority of actual researchers, those that they claim to be enabling and helping with this card will not be able to afford it. It all too easy to forget how the "democratization" of supercomputing was NVIDIA's slogan very ambitious and cool [1] that has over time turned into a marketing claim [2] that offers little in terms of bottom-up enabling the community. [1] http://gpgpu.org/static/asplos2008/ASPLOS08-1-intro-overview.pdf http://gpgpu.org/static/asplos2008/ASPLOS08-1-intro-overview... [2] http://assets.nvidia.com/nv/tesla/pdf/NVIDIA_Accelerate%20Your%20Datacenter%20with%20Tesla%20P100_Whitepaper_Dec2016.pdf http://assets.nvidia.com/nv/tesla/pdf/NVIDIA_Accelerate%20Yo...