Masood Krohy at April 30, 2019 event of EDPP Montreal (montrealml.dev/data)
Title: Supercharging Analytics with GPUs: OmniSci/cuDF vs Postgres/Pandas/PDAL
Presentation/Demo video: check out PatternedScience's YouTube channel at https://www.youtube.com/channel/UCjbEIZlS2DA45Bswi5EXWRw
Summary: GPUs are known to significantly accelerate machine learning model training speeds, especially when using deep learning libraries like TensorFlow. But did you know that there are now solid options to also accelerate data analytics workloads, BI tools and dashboards with the help of GPUs? Join us for a presentation of performance benchmarks of GPU-based options and their CPU-based counterparts. We compare the performance that one could get from OmniSci Core DB (a GPU database) compared to the performance of Postgres DB (for data analytics) and PDAL (for LiDAR processing). On the in-memory side, we benchmark cuDF (NVIDIA's GPU DataFrame) against the widely popular Pandas DataFrame. We will share results and include some code walk-throughs and live benchmarking. Coming out of this technical talk, you will have insight regarding how GPUs can accelerate your data analytics and geospatial workloads.
Code on GitHub: https://github.com/patternedscience/GPU-Analytics-Perf-Tests
Bio: Masood Krohy is a Data Science Platform Architect/Advisor and most recently acted as the Chief Architect of UniAnalytica, an advanced data science platform with wide, out-of-the-box support for time-series and geospatial use cases. He has worked with several corporations in different industries in the past few years to design, implement and productionize Deep Learning and Big Data products. He holds a Ph.D. in computer engineering.