1brc VS bitcoin_ancestries

There are some good ideas for this type of problem here: https://github.com/dannyvankooten/1brc

After you deal with parsing and hashes, basically you are IO limited so mmap helps. A reasonable guess is that even for the optimal CUDA implementation, because there is no compute to speak of other than a hashmap, the starting of kernels and transfer of data to the GPU would likely add a noticeable bottleneck and make the optimal CUDA code slower than this pure C code.

c dominates every other language again...https://github.com/dannyvankooten/1brc#submitting

You can run the bin/create-sample program from this C implementation here: https://github.com/dannyvankooten/1brc

It’s just the city names + averages from the official repository using a normal distribution to generate 1B random rows.

I thought this was an illustrative example of how to process big datasets. We could easily have a statistic per e.g. bitcoin address in a different problem, see https://github.com/afiodorov/bitcoin_ancestries .

I struggle a lot with this toy problem. Without constraints too trivial to pay attention to; then no one seems to agree on potential real-world constraints.