Driving Telematics Analytics to Extreme Scale and Interactive Speed

According to a major car manufacturer, modern vehicles are collecting and sharing more than 25 gigabytes of data per hour, from dozens of sensors focused inside and outside the car. Compound that rate of collection across the growing fleets of connected vehicles, and the automotive industry is facing a stiff new challenge: making hundreds of billions of location-intelligent data points comprehensible, actionable, and predictive. GPUs running OmniSci's extreme analytics platform are uniquely capable of solving this problem, with orders-of-magnitude faster SQL queries, and full-fidelity rendering on the GPU. In this talk, Aaron Williams will use a real-world example to share best practices for analyzing a large dataset of driving behavior, to lower risk and cultivate better drivers.

Transcript

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8. 48% * “2016 Telematics Patterns, Trends and Challenges” http://fleetanswers.com/sites/default/files/2016%20Telematics%20Survey%20Report_0.pdf
9. * “2016 Telematics Patterns, Trends and Challenges” http://fleetanswers.com/sites/default/files/2016%20Telematics%20Survey%20Report_0.pdf
11. * open source for single node github.com/omnisci/mapd-core
16. 10111010101001010110101101010101 00110101101101010101010101011101 Traditional DBs can be highly inefficient • Each operator in SQL treated as a separate function • Incurs tremendous overhead and prevents vectorization OmniSci compiles queries w/LLVM to create one custom function • Queries run at speeds approaching hand-written functions • LLVM enables generic targeting of different architectures (GPUs, X86, ARM, etc). • Code can be generated to run query on CPU and GPU simultaneously
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20. Thank you! Any Questions?