Large-scale GPU-Accelerated Data Visualization with MapD | SF Bay ACM Chapter Meetup

GPU-powered in-memory databases and analytics platforms are the logical successor to CPU in-memory systems, largely due to recent increases in the onboard memory available on GPUs. With sufficient memory, GPUs possess numerous advantages over CPUs, including much greater compute and memory bandwidth, as well as a native graphics pipeline for visualization. In this talk, Veda Shankar, Developer Advocate at MapD, will demo how MapD is able to leverage multiple GPUs per server to extract orders-of-magnitude performance increases over CPU-based systems, bringing interactive querying and visualization to multi-billion (with a ‘b’) row datasets.

Transcript

1. © MapD 2018 Large-scale GPU-Accelerated Data Visualization with MapD Veda Shankar | April 23, 2018
2. © MapD 2018 Veda Shankar Sr Developer Advocate , MapD Community veda.shankar@mapd.com slides: https://speakerdeck.com/mapd
3. © MapD 2018 MapD is the analytics platform created for GPUs
4. © MapD 2018 MapD: software optimized for the fastest hardware
5. © MapD 2018 MapD Core: Query Compilation with LLVM
6. © MapD 2018 MapD Immerse: Hybrid Rendering
7. © MapD 2018 Categories of common MapD use cases 7 Operational Analytics • Thwart Banking Fraud • Scan for Cyber Threats • Fine-tune Advertising • Maintain the Utility Grid Geospatial Analytics • Monitor Networks • Ready Logistics • Forecast Micro-weather Data Science • Model Financial Markets • Predict Maintenance • Predict Staffing Levels
8. © MapD 2018 © MapD 2018 8 DEMO Exploring Google Analytics with MapD
9. © MapD 2018 Advanced memory management Three-tier caching to GPU RAM for speed and to SSDs for persistent storage 9 SSD or NVRAM STORAGE (L3) 250GB to 20TB 1-2 GB/sec CPU RAM (L2) 32GB to 3TB 70-120 GB/sec GPU RAM (L1) 24GB to 256GB 1000-6000 GB/sec Hot Data Speedup = 1500x to 5000x Over Cold Data Warm Data Speedup = 35x to 120x Over Cold Data Cold Data COMPUTE LAYER STORAGE LAYER Data Lake/Data Warehouse/System Of Record
10. © MapD 2018 The GPU Open Analytics Initiative (GOAI) Seamless data interchange framework in GPU memory 1 0
11. © MapD 2018 The GPU Open Analytics Initiative (GOAI) Creating common data frameworks to accelerate data science on GPUs 1 1 /mapd/pymapd /gpuopenanalytics/pygdf
12. © MapD 2018 Machine Learning Pipeline 12 Personas in Analytics Lifecycle (Illustrative) Business Analyst Data Scientist Data Engineer IT Systems Admin Data Scientist / Business Analyst Data Preparation Data Discovery & Feature Engineering Model & Validate Predict Operationalize Monitoring & Refinement Evaluate & Decide GPUs
13. © MapD 2018 • We’ve published a few notebooks showing how to connect to a MapD database and use an ML algorithm to make predictions 13 Github ML Examples /gpuopenanalytics/demo-docker /mapd/mapd-ml-demo
14. © MapD 2018 Quick Code Walkthrough
15. © MapD 2018 Coming Soon 15 Geospatial Data Types and Functions • Early access available now LIDAR Data • Working with Michael Flaxman (Geodesign Technologies) • An open dataset from the state of Florida with several billion points Open Source MapD Core Working Group Forming at FOSS4G • New features coming soon to the open source SQL engine • If you’re interested in participating, talk with the MapD team
16. © MapD 2018 © MapD 2018 • community.mapd.com Ask questions and share your experiences • mapd.com/cloud Try 14-day free trial, no credit card needed • mapd.com/demos Play with our demos • mapd.com/platform/download-community/ Get our free Community Edition and start playing 16 Next Steps
17. © MapD 2018 THANK YOU Any questions?