The query itself it's a perfect hash (assuming the station is dictionary encoded) and takes around 100ms on a gh-200 (the Gpu is a single h100 with 132 SMs) wit

The query itself it's a perfect hash (assuming the station is dictionary encoded) and takes around 100ms on a gh-200 (the Gpu is a single h100 with 132 SMs) with a query that's concurrency constrained. The same level of performance can be obtained using an Ada like an L40s or and RTX 4090. The transfer across the nvlink connecting the Cpu and GPU on a gh-200 after the parse and encoding of the source CSV takes a negligible amount of time given the 500 gb/sec of system memory bandwidth and the 900 GB /sec interconnection between Cpu and Gpu. So the problem is disk bandwidth that's going to limit the performance of the Gpu kernel. The faster solution should be parse the Csv with a gpu kernel using namp and Managed memory (?) encode the station into a interger or a small integer. The min and max value can be used to create keyless perfect hash table for each SM to limit the concurrency on global memory using 32bit atomic operations for min, max, count and sum and then do a final reduction on the Gpu. I don't think that is needed more then 1 modern gpu for this, especially if you are on a modern hardware like the gh-200. I'm running this kind of aggregates on a gh-200 using 10 Billion of records having the data in Gpu or Cpu memory using our software (heavydb) for testing purposes in the last two weeks