Well that's a very pessimistic C++ baseline - using iostreams. The CUDA solution presented here seems to me like it's a bit of an old-timey approach, treating t

Well that's a very pessimistic C++ baseline - using iostreams. The CUDA solution presented here seems to me like it's a bit of an old-timey approach, treating the GPU as an accelerator. The implementation first prepares a million tasks, then submits them as kernel invocations which perform the work. The first phase already takes over two seconds, the second phase takes over ten seconds. Resulting runtime is 16s, 60x faster than iostreams. This is similar to how GPU acceleration is bolted on in a ton of commercial software, you look at hotspots and then move just those over to the accelerator. For example outsourcing some linear algebra in a FEM solver. That's a sensible approach for trying to accelerate legacy software, but it's not very effective and leads to poor utilization ("copy mat, copy mat, copy mat copy copy copy mat" - Dave Cutler about GPU acceleration). I think a) you can do all of this on the GPU b) this should be limited by PCIe bandwidth for getting the file contents into VRAM. The 1BRC data set is 14 GB. So this is processing that file at about 1 GB/s using both CPU and GPU.