The SQL database dates back to the 1970s and has been an ANSI standard since the 1980s, but that doesn’t mean the technology sits still. It is still changing, and one of those ways as GPU-accelerated databases.
Relational databases have grown in size to data sets that measure in the petabytes and beyond. Even with the advent of 64-bit computing and terabytes of memory for increased processing, that’s still a lot of data to chew through—and CPUs can only manage so much. That’s where GPUs have come in.
GPUs have morphed from their original mission of accelerating gaming to accelerating almost everything. Nvidia has pivoted masterfully to become synonymous with artificial intelligence, a process that requires vast amounts of data processed in parallel and other tasks that can be parallelized well. AMD is starting to play catchup, but Nvidia has a long lead.
When it comes to cores, it’s not even close. Xeon CPUs have a maximum of 22 cores. AMD Epyc has 32 cores. The Nvidia Volta architecture has 5,120 cores. Now imagine more than 5,000 cores running in parallel on data and it’s clear why GPUs have become so popular for massive compute projects.
So a new class of databases has emerged, written from the ground up to support and embrace GPUs and their massive parallel processing capabilities. These databases are enabling new levels of data processing, analytics and real-time Big Data as they can handle data sets that regular CPU-powered databases simply cannot.
