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You can plug any compute kernel you want into spark, that's not a pro or con of R. Column stores are standard in any analytics pipeline today. They make up Pyt

You can plug any compute kernel you want into spark, that's not a pro or con of R. Column stores are standard in any analytics pipeline today. They make up Python's Pandas, R's dplyr, and Java's DataFrame. How or why does R stand out for 'massive amounts of data'? R does not have have meaningful out of core compute offerings that compare with something like Dask. R does not at all have cluster compute offerings that compare to Dask Distributed. If you want to know what real performance looks like, check out Python's cudf which will shortly fully match the Pandas api. That raytracing example you linked would run at interactive rates with cudf, I really don't see any basis for perf arguments in R's favour, and 'massive data' arguments are laughable here. Whatever advantages R has, perf or scalability are definitely not amongst them.

tylermw 7y ago
> That raytracing example you linked would run at interactive rates with cudf, I really don't see any basis for perf arguments in R's favour, and 'massive data' arguments are laughable here. I don't see how the "GPU DataFrames" provided in cuDF would enhance a raytracer in any way.
- bicubic 7y ago
  You don’t see how a gpu accelerated numeric array would speed up ray tracing?
  - tylermw 7y ago
    The bottlenecks for raytracing are primarily in scene traversal/intersection testing--which does not benefit from a GPU-accelerated array structure.
    - bicubic 7y ago
      You are literally as wrong as it is possible to be wrong. http://hoxxep.github.io/webgl-ray-tracing-demo/ http://hoxxep.github.io/webgl-ray-tracing-demo/ https://devblogs.nvidia.com/accelerated-ray-tracing-cuda/ https://devblogs.nvidia.com/accelerated-ray-tracing-cuda/
      - tylermw 7y ago
        No, I'm not. I'm well aware of CUDA being used to accelerate raytracing. That cannot be accomplished by simply providing a GPU-accelerated data frame structure, as cuDF provides.
Annatar 7y ago
You are arguing for Python and speed in the same breath? If you want portable speed, you better "warm up a chair" and master Fortran. Bonus: modern Fortran is a joy to develop in, far more fun than Python. And you get to compile to machine code, either for a processor or a GPU.

tylermw 7y ago

> That raytracing example you linked would run at interactive rates with cudf, I really don't see any basis for perf arguments in R's favour, and 'massive data' arguments are laughable here. I don't see how the "GPU DataFrames" provided in cuDF would enhance a raytracer in any way.

bicubic 7y ago
You don’t see how a gpu accelerated numeric array would speed up ray tracing?
- tylermw 7y ago
  The bottlenecks for raytracing are primarily in scene traversal/intersection testing--which does not benefit from a GPU-accelerated array structure.
  - bicubic 7y ago
    You are literally as wrong as it is possible to be wrong. http://hoxxep.github.io/webgl-ray-tracing-demo/ http://hoxxep.github.io/webgl-ray-tracing-demo/ https://devblogs.nvidia.com/accelerated-ray-tracing-cuda/ https://devblogs.nvidia.com/accelerated-ray-tracing-cuda/
    - tylermw 7y ago
      No, I'm not. I'm well aware of CUDA being used to accelerate raytracing. That cannot be accomplished by simply providing a GPU-accelerated data frame structure, as cuDF provides.

Annatar 7y ago

You are arguing for Python and speed in the same breath? If you want portable speed, you better "warm up a chair" and master Fortran. Bonus: modern Fortran is a joy to develop in, far more fun than Python. And you get to compile to machine code, either for a processor or a GPU.