Most of the benchmarks have dask and cuDF isolated, but i can use them together. Wouldn't Dask with cuDF be faster than polars?!
Also, Polars only runs if the data fits in memory, but this isn't the case with dask. So why is there https://h2oai.github.io/db-benchmark/ an out of memory indication for dask?
Different dataframe libraries have their strengths and weaknesses. For example, see this blog post for a comparison of different libraries, esp. from a scaling pandas perspective.
Dask Dataframe comes with some default assumptions on how best to divide the workload among multiple tasks. If these assumptions are not be valid for the particular use-case, then it's not uncommon to see memory-related errors.
