GPU-Accelerated Data Analytics in Python

GPU-Accelerated Data Analytics in Python

PyCon US via YouTube Direct link

CLX Cyber Log Accelerators

26 of 27

26 of 27

CLX Cyber Log Accelerators

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GPU-Accelerated Data Analytics in Python

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  1. 1 Intro
  2. 2 RAPIDS GPU Accelerated Data Analytics in Python
  3. 3 Data Processing Evolution Faster data access, less data movement
  4. 4 Data Movement and Transformation What if we could keep data on the GPU?
  5. 5 Faster Speeds, Real-World Benefits
  6. 6 Speed, Ease of use, and Iteration The Way to Win a Data Science
  7. 7 Open Source Data Science Ecosystem Familiar Python APIs
  8. 8 RAPIDS End-to-End Accelerated GPU Data Science
  9. 9 RAPIDS GPU Accelerated data wrangling and feature engineering
  10. 10 ETL Technology Stack
  11. 11 Benchmarks: single-GPU Speedup vs. Pandas
  12. 12 ETL: the Backbone of Data Science String Support
  13. 13 Extraction is the Cornerstone CUDF I/O for Faster Data Loading
  14. 14 ML Technology Stack
  15. 15 RAPIDS matches common Python APIs
  16. 16 RAPIDS RELEASE SELECTOR
  17. 17 Forest Inference Taking models from training to production
  18. 18 Goals and Benefits of cuGraph Focus on Features and User Experience
  19. 19 Graph Technology Stack
  20. 20 Algorithms GPU accelerated Network
  21. 21 Multi-GPU PageRank Performance PageRank portion of the HiBench benchmark suite
  22. 22 cuSpatial Technology Stack
  23. 23 Speed of Light Performance - V100
  24. 24 Efficient Memory Handling
  25. 25 Marriage of Deep Learning and RF Data
  26. 26 CLX Cyber Log Accelerators
  27. 27 CLX Components Notebook Examples, SIEM Integrations, Workflows, Primitives

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