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Actually AMD gpus are the most cost effective gpus and have supported full FP16/8 since 2 years. But people are locked in the Nvidia proprietary jail and no one

Actually AMD gpus are the most cost effective gpus and have supported full FP16/8 since 2 years. But people are locked in the Nvidia proprietary jail and no one seems to care... That is really sad and against the consummer interest, but also, deep learning will never become mainstream if it can only run on 5% of the world hardware (Nvidia).

keldaris 8y ago
I agree with your broader point, but stating that Nvidia comprises a mere 5% of the world's hardware is disingenous in a post where you've only talked about GPUs. In actual fact, Nvidia has the overwhelming majority of the high end GPU market, especially in HPC and ML. And as much as I dislike the proprietary lock-in, they have by far the superior software ecosystem.
- The_rationalist 8y ago
  Actually nvidia isn't the high end leader if you account console APUs (almost all being from AMD). And most gpus in the world are from igpus, apus or ARM SoCs. Dgpu IS a small market, High end Dgpu is a few percents max.
gradys 8y ago
Deep learning generally doesn't run on AMD GPUs, but it runs on a lot more than only Nvidia GPUs. If we're only counting model training, it runs on CPUs, Google's TPUs, FPGAs, whatever other secret datacenter ASICs are out there, various DL-specific mobile chips, etc. Way more than 5% of the world's hardware can run inference with deep neural nets, which is the important thing for mass adoption, and definitely more than only Nvidia GPUs can run training.
ratsimihah 8y ago
I haven't had a great experience using anything else than CUDA, e.g. OpenCL. Then again maybe I just lack the skills :x
TTPrograms 8y ago
If AMD developed CUDA transpilers / DL backends / CUDNN equivalents people would switch over in a heartbeat. It's fully their fault for not investing in the software. If they did that and had a card that got 2x performance / $ or more I would switch in a heartbeat.
- JoshuaJB 8y ago
  AMD has a CUDA Transpiler (https://github.com/ROCm-Developer-Tools/HIP https://github.com/ROCm-Developer-Tools/HIP) and a multitude of other open source libraries through GPUOpen ( https://gpuopen.com/professional-compute/ https://gpuopen.com/professional-compute/). The quality and open source nature of their tools has resulted in much of my research group (real-time vision) increasingly and voluntarily moving to work on AMD platforms (we were previously almost exclusively using CUDA).
  - deleted 8y ago
    [deleted]
  - ratsimihah 8y ago
    But the GPU needs to support ROCm, which is quite recent right? e.g. The MBP 2015's Radeon m370x doesn't seem to support it.
    - gcb0 8y ago
      less recent than a 1080 or 2080ti
      - ratsimihah 8y ago
        2080 Ti is brand new :/
- auslander 8y ago
  Here is it: https://github.com/ROCm-Developer-Tools/HIP https://github.com/ROCm-Developer-Tools/HIP Also, AMD does not limit FP performance on consumer cards.
kbumsik 8y ago
> Actually AMD gpus are the most cost effective gpus. No, AMD gpu is zero cost effective because Tensorflow does not support AMD gpus. > But people are locked in the Nvidia proprietary jail and no one seems to care... Sounds like you want to blame the users, but this is because Nvidia highly invested on GPGPU and Cuda since more than 10 years ago, while AMD did focus on something else like HSA. It is AMD’s fault.
- montalbano 8y ago
  > No, AMD gpu is zero cost effective because Tensorflow does not support AMD gpus. Incorrect, see: https://github.com/ROCmSoftwarePlatform/tensorflow-upstream https://github.com/ROCmSoftwarePlatform/tensorflow-upstream
  - shaklee3 8y ago
    Do you know where we can find some benchmarks?
- sabalaba 8y ago
  I agree with kbumsik here. AMD only has themselves to blame. They have great hardware and fantastic theoretical benchmarks. Heck, even their SGEMMs are really fast and in line with the 15TFlops of FP32 on the VEGA 64s that we've benchmarked. However, it comes down to software ecosystem and optimizations for common deep learning subroutines. MIOpen[1] is a step in this direction but still causes the VEGA 64 + MIOpen to be 60% of the performance of a 1080 Ti + CuDNN based on benchmarks we've conducted internally at Lambda. Let that soak in for a second: the VEGA 64 (15TFLOPS theoretical peak) is 0.6x of a 1080 Ti (11.3TFLOPS theoretical peak). MIOpen is very far behind CuDNN. Lisa Su, if you're reading this, please give the ROCm team more budget! 1. https://github.com/ROCmSoftwarePlatform/MIOpen https://github.com/ROCmSoftwarePlatform/MIOpen
  - lostmsu 8y ago
    Or, maybe, a new team.
BoorishBears 8y ago
Do you own AMD stock?
- The_rationalist 8y ago
  Why this question ? No I don't. My message is objective and openCL/sysCL allow to run on All accellerators, not just AMD nor Nvidia.
  - BoorishBears 8y ago
    Because you started your comment with an objective falsehood, NVIDIA has the most cost effective GPUs as long as CUDA king and AMD doesn’t invest more on the software front

keldaris 8y ago

I agree with your broader point, but stating that Nvidia comprises a mere 5% of the world's hardware is disingenous in a post where you've only talked about GPUs. In actual fact, Nvidia has the overwhelming majority of the high end GPU market, especially in HPC and ML. And as much as I dislike the proprietary lock-in, they have by far the superior software ecosystem.

The_rationalist 8y ago
Actually nvidia isn't the high end leader if you account console APUs (almost all being from AMD). And most gpus in the world are from igpus, apus or ARM SoCs. Dgpu IS a small market, High end Dgpu is a few percents max.

gradys 8y ago

Deep learning generally doesn't run on AMD GPUs, but it runs on a lot more than only Nvidia GPUs. If we're only counting model training, it runs on CPUs, Google's TPUs, FPGAs, whatever other secret datacenter ASICs are out there, various DL-specific mobile chips, etc. Way more than 5% of the world's hardware can run inference with deep neural nets, which is the important thing for mass adoption, and definitely more than only Nvidia GPUs can run training.

ratsimihah 8y ago

I haven't had a great experience using anything else than CUDA, e.g. OpenCL. Then again maybe I just lack the skills :x

TTPrograms 8y ago

If AMD developed CUDA transpilers / DL backends / CUDNN equivalents people would switch over in a heartbeat. It's fully their fault for not investing in the software. If they did that and had a card that got 2x performance / $ or more I would switch in a heartbeat.

JoshuaJB 8y ago
AMD has a CUDA Transpiler (https://github.com/ROCm-Developer-Tools/HIP https://github.com/ROCm-Developer-Tools/HIP) and a multitude of other open source libraries through GPUOpen ( https://gpuopen.com/professional-compute/ https://gpuopen.com/professional-compute/). The quality and open source nature of their tools has resulted in much of my research group (real-time vision) increasingly and voluntarily moving to work on AMD platforms (we were previously almost exclusively using CUDA).
- deleted 8y ago
  [deleted]
- ratsimihah 8y ago
  But the GPU needs to support ROCm, which is quite recent right? e.g. The MBP 2015's Radeon m370x doesn't seem to support it.
  - gcb0 8y ago
    less recent than a 1080 or 2080ti
    - ratsimihah 8y ago
      2080 Ti is brand new :/
auslander 8y ago
Here is it: https://github.com/ROCm-Developer-Tools/HIP https://github.com/ROCm-Developer-Tools/HIP Also, AMD does not limit FP performance on consumer cards.

kbumsik 8y ago

> Actually AMD gpus are the most cost effective gpus. No, AMD gpu is zero cost effective because Tensorflow does not support AMD gpus. > But people are locked in the Nvidia proprietary jail and no one seems to care... Sounds like you want to blame the users, but this is because Nvidia highly invested on GPGPU and Cuda since more than 10 years ago, while AMD did focus on something else like HSA. It is AMD’s fault.

montalbano 8y ago
> No, AMD gpu is zero cost effective because Tensorflow does not support AMD gpus. Incorrect, see: https://github.com/ROCmSoftwarePlatform/tensorflow-upstream https://github.com/ROCmSoftwarePlatform/tensorflow-upstream
- shaklee3 8y ago
  Do you know where we can find some benchmarks?
sabalaba 8y ago
I agree with kbumsik here. AMD only has themselves to blame. They have great hardware and fantastic theoretical benchmarks. Heck, even their SGEMMs are really fast and in line with the 15TFlops of FP32 on the VEGA 64s that we've benchmarked. However, it comes down to software ecosystem and optimizations for common deep learning subroutines. MIOpen[1] is a step in this direction but still causes the VEGA 64 + MIOpen to be 60% of the performance of a 1080 Ti + CuDNN based on benchmarks we've conducted internally at Lambda. Let that soak in for a second: the VEGA 64 (15TFLOPS theoretical peak) is 0.6x of a 1080 Ti (11.3TFLOPS theoretical peak). MIOpen is very far behind CuDNN. Lisa Su, if you're reading this, please give the ROCm team more budget! 1. https://github.com/ROCmSoftwarePlatform/MIOpen https://github.com/ROCmSoftwarePlatform/MIOpen
- lostmsu 8y ago
  Or, maybe, a new team.

BoorishBears 8y ago

Do you own AMD stock?

The_rationalist 8y ago
Why this question ? No I don't. My message is objective and openCL/sysCL allow to run on All accellerators, not just AMD nor Nvidia.
- BoorishBears 8y ago
  Because you started your comment with an objective falsehood, NVIDIA has the most cost effective GPUs as long as CUDA king and AMD doesn’t invest more on the software front