4 ms·

What exactly are you missing when hipifying your CUDA codebase? For most of the software I've looked at this has been a breeze, mostly consisting of srtting up

by tovej 3y ago

What exactly are you missing when hipifying your CUDA codebase? For most of the software I've looked at this has been a breeze, mostly consisting of srtting up toolchains. Or do you mean the profiler tooling? I hear everyone say that AMD doesn't have the software, but I'm a little confused --- have you tried HIP? And have you tried the automatic CUDA - HIP translation? What's missing?

dotnet00 3y ago
I think they're referring to support in general. Technically HIP exists, but it's a pain to actually use, it has limited hardware support, is far less reliable in terms of supporting older hardware, needs a new binary for every new platform and so on. CUDA runs on pretty much every NVIDIA GPU, this year they dropped support for GPUs released 9 years ago, and older binaries are very likely to be forward compatible. Meanwhile my Radeon VII is already unsupported despite still being pretty capable (especially for FP64), and my 5700XT was never supported at all (I may be mixing this up with support for their math libraries), everyone was just led on with promises of upcoming support for 2 years. So "AMD has the software now" is not really convincing.
- tovej 3y ago
  I suppose if you're talking about consumer cards, I agree, support is often missing. But if we're talking datacenter GPUs, the software is there. Data centers is where most GPGPU computing happens after all. It's not ideal when it comes to hobby development, but if you're working in a professional capacity I'm assuming you're working with a modern HPC or AI cluster.
  - dotnet00 3y ago
    Well, to provide my own experience bringing GPU acceleration to scientific computation software, AMD got passed on because even if the software was there at the time (it wasn't), there was no way to justify spending a bunch of money buying everyone workstation AMD GPUs when we could just start by picking up basic consumer NVIDIA cards to work on for anyone who didn't happen to have one already and then worry about buying more suitable cards if needed. Of course the end goal was to run on a large HPC cluster we had access to, but for efficient development, support on personal machines was necessary. My personal dual 3090 setup has been invaluable for getting through debugging and testing before dealing with the queueing system on the cluster (Side note: it also ended up revealing another important benefit of consumer side support for GPGPU, a 3090 was easily matching the performance of a single node of the CPU-only version of the cluster, thus massively bringing down the cost of entry to an otherwise computationally restrictive topic).
    - tails4e 3y ago
      This is a very valid point. 100% AMD need better support of consumer cards to get research, university and everyday folks using their SW. The high end consumer7900 xtx are supported now, and a lot of other cards not officially supported actually work. (I've a colleague who tried it on his home computer and got it working, despite that card not being on the official list) .. . Still, AMD need to be getting more cards working ASAP
deleted 3y ago
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by tovej 3y ago

dotnet00 3y ago

I think they're referring to support in general. Technically HIP exists, but it's a pain to actually use, it has limited hardware support, is far less reliable in terms of supporting older hardware, needs a new binary for every new platform and so on. CUDA runs on pretty much every NVIDIA GPU, this year they dropped support for GPUs released 9 years ago, and older binaries are very likely to be forward compatible. Meanwhile my Radeon VII is already unsupported despite still being pretty capable (especially for FP64), and my 5700XT was never supported at all (I may be mixing this up with support for their math libraries), everyone was just led on with promises of upcoming support for 2 years. So "AMD has the software now" is not really convincing.

tovej 3y ago
I suppose if you're talking about consumer cards, I agree, support is often missing. But if we're talking datacenter GPUs, the software is there. Data centers is where most GPGPU computing happens after all. It's not ideal when it comes to hobby development, but if you're working in a professional capacity I'm assuming you're working with a modern HPC or AI cluster.
- dotnet00 3y ago
  Well, to provide my own experience bringing GPU acceleration to scientific computation software, AMD got passed on because even if the software was there at the time (it wasn't), there was no way to justify spending a bunch of money buying everyone workstation AMD GPUs when we could just start by picking up basic consumer NVIDIA cards to work on for anyone who didn't happen to have one already and then worry about buying more suitable cards if needed. Of course the end goal was to run on a large HPC cluster we had access to, but for efficient development, support on personal machines was necessary. My personal dual 3090 setup has been invaluable for getting through debugging and testing before dealing with the queueing system on the cluster (Side note: it also ended up revealing another important benefit of consumer side support for GPGPU, a 3090 was easily matching the performance of a single node of the CPU-only version of the cluster, thus massively bringing down the cost of entry to an otherwise computationally restrictive topic).
  - tails4e 3y ago
    This is a very valid point. 100% AMD need better support of consumer cards to get research, university and everyday folks using their SW. The high end consumer7900 xtx are supported now, and a lot of other cards not officially supported actually work. (I've a colleague who tried it on his home computer and got it working, despite that card not being on the official list) .. . Still, AMD need to be getting more cards working ASAP

deleted 3y ago

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