cudf-spark

operations. These aren't physical/logical limitations, it's just where the software is right now: https://github.com/NVIDIA/spark-rapids/issues Multi-column UDF support would likely require some compiler-esque work in Scala (which I happen to have experience in). A few things I expect to be "very" good: joins, string aggregations (empirically), sorting (clustering). Operations which stress memory bandwidth will likely be "surprisingly" good (surprising to most people). Otherwise, Nvidia has published a bunch of really-good-looking public data, along with some other public companies. Outside of Spark, I think many people underestimate how "low-latency" GPUs can be. 100 microseconds and above is highly likely to be a good fit for GPU acceleration in general, though that could be as low as 10 microseconds (today).

are only good for AI, graphics, and maybe image processing (maybe! someone actually told me they thought GPUs are bad for image processing!) Traditional data processing doesn’t come to mind. But actually GPUs are good for this as well! At a high level, big data processing is a high-throughput, massively parallel workload. GPUs are a type of hardware specialized for this, are highly programmable, and (now) happen to be highly available on the cloud! Even better, GPU memory is tuned for bandwidth over raw latency, which only improves their throughput capabilities compared to a CPU. And by just playing with cloud cost calculators for a couple of minutes, it's clear that GPUs are cost-effective even on the major clouds. To be honest, I thought using GPUs for SQL processing would have taken off by now, but it hasn't. So, just over a year ago, I started working on actually deploying a cloud-based data platform powered by GPUs (i.e. Spark-RAPIDS), spurred by a friend-of-a-friend(-of-a-friend) who happened to have BigQuery cost concerns at his startup. After getting a proof of concept done and a letter of intent... well, nothing happened! Even after over half a year. But then, something magical did happen: their cloud credits ran out! And now, they're saving over 60% off of their BigQuery bill by using ParaQuery, while also being 2x faster -- with zero data migration needed (courtesy of Spark's GCS connector). By the way, I'm not sure about other people's experiences but... we're pretty far from being IO-bound (to the surprise of many engineers I've spoken to). I think that the future of high-throughput compute is computing on high-throughput hardware. If you think so too, or you have scaling data challenges, you can sign up here: https://paraquery.com/waitlist. Sorry for the waitlist, but we're not ready for a self-serve experience just yet—it would front-load significant engineering and hardware cost. But we’ll get there, so stay tuned! Thanks for reading! What have your experiences been with huge ETL / processing loads? Was cost or performance an issue? And what do you think about GPU acceleration (GPGPU)? Did you think GPUs were simply expensive? Would love to just talk about tech here!