[db tech showcase Tookyo 2018] #dbts2018 #B24 『Speed Meets Scale: Analyzing & Visualizing Billions of Data Points with GPUs』
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The document presents a showcase on leveraging GPU technology for analyzing and visualizing vast datasets, discussing various roles in the analytics lifecycle. It highlights the performance benefits of GPUs over CPUs and introduces advanced memory management for optimized data handling. Additionally, it provides information on MapD's products and community resources for users interested in exploring GPU-accelerated analytics.
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[db tech showcase Tookyo 2018] #dbts2018 #B24 『Speed Meets Scale: Analyzing & Visualizing Billions of Data Points with GPUs』
- 1. Speed Meets Scale: Analyzing & Visualizing Billions of Data Points with GPUs DB Tech Showcase | Tokyo | September 20, 2018
- 2. © MapD 2018 Aaron Williams VP of Global Community @_arw_ aaron@mapd.com /in/aaronwilliams/ /williamsaaron slides: https://speakerdeck.com/mapd
- 3. © MapD 2018 3 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 Friday, Sept 21
- 8. The Fastest Software Designed for the Fastest Hardware HARNESS GPUs
- 9. © MapD 2018 9 GPU ProcessingCPU Processing 40,000 Cores 20 Cores *fictitious example Latency Throughput CPU 1 ns per task (1 task/ns) x (20 cores) = 20 tasks/ns GPU 10 ns per task (0.1 task per ns) x (40,000 cores) = 4,000 task per ns Latency: Time to do a task. | Throughput: Number of tasks per unit time.
- 10. © MapD 2018 10 * open source for single node github.com/mapd/mapd-core
- 11. D E M O S https://www.mapd.com/demos/
- 12. © MapD 2018 Advanced memory management Three-tier caching to GPU RAM for speed and to SSDs for persistent storage 1 2 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
- 13. © MapD 2018 MapD Core: Query Compilation with LLVM
- 14. © MapD 2018 MapD Immerse: Hybrid Rendering
- 15. EXTREME ANALYTICS: EXTREME DATA & EXTREME EXPERIENCE
- 17. © MapD 2018 TOP-TIER VENTURE BACKING USED BY 100+ GLOBAL ORGS$37 MILLION IN FUNDING OPEN-SOURCE COMMUNITY About MapD 17
- 18. • mapd.com/demos Play with our demos - everything demo you saw in this talk was live! • mapd.cloud Get a MapD instance in less than 60 seconds • www.mapd.com/platform/downloads/ Download the Community Edition • community.mapd.com Ask questions and share your experiences Next Steps
- 19. © MapD 2018 Aaron Williams VP of Global Community @_arw_ aaron@mapd.com /in/aaronwilliams/ /williamsaaron slides: https://speakerdeck.com/mapd Thank you! Questions?