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Comment by bt1a | Hacker News Reader

pardon but aren't disks usually the bottleneck? im all for CUDA acceleration and CUDA accelerating culture

Parent post was talking about an 8GB dataset.

8GB isn't even CPU RAM these days. That's GPU super-mega-awesome ram. Ordinary Server CPUs are regularly pushing 2TB capacities.

GPUs are in the 8GB to 32GB typically, at least for smaller and more regular GPUs. This GPU RAM is also well known to be at least 10x the bandwidth of CPU RAM.

Yeah, I have a GPU from almost six years ago in my desktop that has twice that much VRAM. Less than a year ago my wife got a 5070 Ti with the same for around $750 without needing to wait for it to be in stock or anything. I'm inclined to think that for a server that needs a GPU, even 32 GB would probably be considered small.

Which is why a good query plan is so important, so that as much disk I/O can be avoided as possible (predicate push down, index elimination, join ordering, partition/scan pruning). Like the old CTE optimization fence problem.

I remember projects like PG-Strom back in the day, very cool stuff

not necessarily, no. With SSDs you get much better IOPS for cold data, and many datasets fit in RAM. So a lot of (OLTP/HTAP) workloads can become CPU-bound due to sorting/hashing - bread and butter of joins.

Though many use network block storage (e.g. EBS), which is significantly slower than an SSD.

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