[FEA] spil data from main memory to disk memory

[jangorecki]

I have dataset stored in CSV of 3 different sizes

• 0.5 GB
• 5 GB
• 50 GB

My machine has 120 GB of main memory and 11 GB of gpu memory.
cudf is capable to make computation on 0.5 GB data using just gpu memory.
If I want to run computations on 5 GB data I can set cudf.set_allocator("managed"). It works really nice and fast, more importantly it allows me to run computation on my medium data.
Problem is when I attempt to run computation on 50 GB data I am getting following error:

terminate called after throwing an instance of 'thrust::system::system_error'
  what():  parallel_for failed: an illegal memory access was encountered

I assume there is not enough of main memory for this computation. This is known python issue for pandas and (in-memory-)dask when attempting to run same computation on that data size.
My feature request is about extending the feature of spilling gpu memory to main memory for a disk memory as well.

[pentschev]

I guess this is more of a question for RMM, but we had a long discussion about this some time ago @jrhemstad , do you have any idea of how managed memory could eventually allow spilling to disk? I don't think that this is something that this is supported currently, given it's a driver level feature.

[kkraus14]

This is out of the scope of cudf. If you need the ability to spill to disk you should use dask-cudf with dask-cuda workers, even with a single GPU. That will handle chunking, spilling from device --> host, and host --> disk.

[jangorecki]

Thank you for claryfing, it is well enough for my case.

[jangorecki]

I am looking for a feature to spill computation vmem->mem->disk using dask_cudf. Is there any better place for this FR?
@kkraus14 could we re-open this issue?

[quasiben]

We have spilling configurations defined in dask-cuda. I would suggest reading over the linked doc and if you have questions, post them in the dask-cuda GH repo

[jangorecki]

@quasiben Thank you, I found existing issue for that there already, linking for future readers: rapidsai/dask-cuda#37