Omnisci is a columnar database that reads a column into GPU memory, in compressed form, allowing for interactive queries on the data. A demo was shown at the GTC keynote this year, by Aaron Williams who gave a talk on vehicle analytics later that I attended last month. A single gpu can load 10million to 50million rows of data and allows interactive querying without indexing.
In one of the vehicle telemetry demos, they obtain vehicle telemetry data from an F1 game that has data output as UDP, 10s of thousands of packets a second – take the binary data off of UDP, and convert it to json and use it as a proxy for real telemetry data. The webserver refreshes every 3-4 seconds. The use case is analysis of increasing amounts of vehicle sensor data as discussed in this video and described in the detailed Omnisci blog post here.
The vehicle analytics demo pipeline consisted of UDP to Kafka, Kafka to JSON, then JSON to OmniSci via pymapd . Kafka serves as a message broker and also for playback of data.
Based on the the GPU loaded data, the database allows queries and stats on different vehicles that are running.
The entire system runs in the cloud on a VM supporting Nvidia GPUs, and can also be run on a local GPU box.