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