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6186 :: Converting Python/Swift to Nim

• chrisheller (orginal) [2020-04-10T11:49:51+02:00] view original

  While doing a little TV binge-watching on quarantine tonight, I decided to convert one of the examples from this blog post, https://tryolabs.com/blog/2020/04/02/swift-googles-bet-on-differentiable-programming/, into Nim.

  The code is posted here, https://github.com/chrisheller/experiments/tree/master/swift-python-performance, and just walks through doing the same changes that the original author makes to convert their Python example to Swift.

  The end result in Nim comes in just slightly faster than the author's Swift code or the C code that they were trying to match, but still looks very close to the original Python code. Insert large caveat here about micro-benchmarks, especially since I'm comparing to the author's stated Swift times since I don't have Swift installed on this laptop.

  Note that I didn't try to make the code more idiomatic Nim, I just started with the original Python code and just made the minimal changes to make it Nim.

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• mratsim (orginal) [2020-04-10T12:11:20+02:00] view original

  I'm following closely on deep learning compilers. I find the technology behind S4TF very interesting in particular their differentiable programming document: https://github.com/apple/swift/blob/master/docs/DifferentiableProgramming.md however:

  • Swift main platform is Mac, and Mac are very poor at machine learning because:
    • No support of OpenMP in their compiler, you need to build gcc/llvm from source making usage of libraries like XgBoost or LightGBM a huge core
    • No Nvidia GPU/Cuda, for better or worse, most of the code is written for Nvidia GPU. AMD is catching up with ROCm but I'm pretty sure you can't install it on Mac because Apple wants to push Metal down the Mac user throats and it's a very limited API.
  • Swift threading model is GrandCentralDispatch / libdispatch. There is no issue in performance but it requires kernel hooks. This means that you need a custom kernel module on Linux, which is very constraining for cloud computing.
  • On Windows you have to use Windows Fibers for threading, seems like a second citizen (Swift for Windows last update was 2018, you have to compile it yourself).

  Also Swift is seriously lacking in multidimensional array, dataframe and a plotting library. Those are the 3 building blocks off a machine learning ecosystem.

  Lastly Google seems to be betting more on the following:

  • Jax https://github.com/google/jax, a compiler for Python with differentiable programming support
  • MLIR https://llvm.org/devmtg/2019-04/slides/Keynote-ShpeismanLattner-MLIR.pdf which got accepted as an LLVM project. AFAIK Chris Lattner has been working more on MLIR than Swift for Tensorflow.

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