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I'm wondering the same. I'd guess that 99% of science code that uses the GPU now depends on CUDA. The other 1% use other third-party GPU frontends which might a

I'm wondering the same. I'd guess that 99% of science code that uses the GPU now depends on CUDA. The other 1% use other third-party GPU frontends which might also be tuned for Nvidia GPUs. I feel that it's good to use the AMD ecosystem to mix up the competition a bit, and the majority of other clusters will still use Nvidia if your application requires it, but I'm just not aware of any momentum at all (in the form of software ecosystems) that target AMD, so library-writers will have to basically start from scratch if this AMD cluster is confirmed.

rl3 7y ago
>... so library-writers will have to basically start from scratch if this AMD cluster is confirmed. I doubt library writers will care beyond anyone involved in the project. That said, it's possible to port CUDA code already with a bit of work, so not quite from scratch in any case: https://github.com/ROCm-Developer-Tools/HIP https://github.com/ROCm-Developer-Tools/HIP
- minxomat 7y ago
  See also PlaidML
  - throw20102010 7y ago
    PlaidML is not what most people want. PlaidML is pretty much only for neural network based machine learning, plus it's primarily a backend for models written in Keras. But it is an option for the narrow use case of neural networks. Contrary to what buzzword happy SV types would have you believe, most "real" HPC work isn't machine learning (r/gatekeeping, I know). Particle physics, computational fluid dynamics, network simulations, etc. Lots of it is already written in CUDA. HIP, using hipify, can translate the already written CUDA code to HIP, which is GPU-agnostic.
    - minxomat 7y ago
      Plaid is just one of 01org's projects. There are dozens of other projects (and compilers), which when combined can be used to build very intricate HPC applications and pipelines. Though for some core projects, most of the tuning development is only available on Intel platforms.
      - throw20102010 7y ago
        1. Your previous comment only mentioned PlaidML. 2. The parent comment was about translating existing code written in CUDA to be used on an AMD GPU, not about developing new software- if it were about developing new software, they'd be starting from scratch with 01.org tools, which is what everyone wants to avoid. 01.org doesn't have any translation tools for this.
tgamblin 7y ago
On the NNSA side of DOE, the codes very intentionally do not depend on CUDA. They use RAJA (https://github.com/llnl/raja https://github.com/llnl/raja) or Kokkos (https://github.com/kokkos/kokkos https://github.com/kokkos/kokkos) to ensure that they're not tied to a particular GPU. Using those frameworks you can easily switch between CUDA, OpenMP, or whatever in a single-source codebase.

rl3 7y ago

>... so library-writers will have to basically start from scratch if this AMD cluster is confirmed. I doubt library writers will care beyond anyone involved in the project. That said, it's possible to port CUDA code already with a bit of work, so not quite from scratch in any case: https://github.com/ROCm-Developer-Tools/HIP https://github.com/ROCm-Developer-Tools/HIP

minxomat 7y ago
See also PlaidML
- throw20102010 7y ago
  PlaidML is not what most people want. PlaidML is pretty much only for neural network based machine learning, plus it's primarily a backend for models written in Keras. But it is an option for the narrow use case of neural networks. Contrary to what buzzword happy SV types would have you believe, most "real" HPC work isn't machine learning (r/gatekeeping, I know). Particle physics, computational fluid dynamics, network simulations, etc. Lots of it is already written in CUDA. HIP, using hipify, can translate the already written CUDA code to HIP, which is GPU-agnostic.
  - minxomat 7y ago
    Plaid is just one of 01org's projects. There are dozens of other projects (and compilers), which when combined can be used to build very intricate HPC applications and pipelines. Though for some core projects, most of the tuning development is only available on Intel platforms.
    - throw20102010 7y ago
      1. Your previous comment only mentioned PlaidML. 2. The parent comment was about translating existing code written in CUDA to be used on an AMD GPU, not about developing new software- if it were about developing new software, they'd be starting from scratch with 01.org tools, which is what everyone wants to avoid. 01.org doesn't have any translation tools for this.

tgamblin 7y ago

On the NNSA side of DOE, the codes very intentionally do not depend on CUDA. They use RAJA (https://github.com/llnl/raja https://github.com/llnl/raja) or Kokkos (https://github.com/kokkos/kokkos https://github.com/kokkos/kokkos) to ensure that they're not tied to a particular GPU. Using those frameworks you can easily switch between CUDA, OpenMP, or whatever in a single-source codebase.