Using GPUs for Lightning Fast Analytics on MapD

We'll learn about MapD - a powerful platform for visualizing large datasets.

GPU-powered in-memory databases and analytics platforms are the logical successors 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 tutorial, Aaron Williams, VP of Community 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 Using GPUs for Lightning Fast Analytics on MapD Aaron Williams May 15, 2018
2. © MapD 2018 Introductions Aaron Williams VP of Global Community @_arw_ aaron@mapd.com /in/aaronwilliams/ /williamsaaron slides: https://speakerdeck.com/mapd/ Randy Zwitch Senior Developer Advocate @randyzwitch randy.zwitch@mapd.com /in/randyzwitch/ /randyzwitch Veda Shankar Senior Developer Advocate @veda_shankar veda.shankar@mapd.com /in/veda-shankar-6260a516/
3. Core Density Makes a Huge Difference 3 GPU Processing CPU 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.
4. © MapD 2018 MapD is the analytics platform created for GPUs
5. © MapD 2018 © MapD 2018 5 DEMO TIME #1
6. © MapD 2018 Advanced memory management Three-tier caching to GPU RAM for speed and to SSDs for persistent storage 6 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
7. © MapD 2018 The GPU Open Analytics Initiative Creating common data frameworks to accelerate data science on GPUs 7 /mapd/pymapd /gpuopenanalytics/pygdf
8. © MapD 2018 And Now … Native Geospatial! 8 First Data Types • POINT • LINE • POLYGON First Functions • DISTANCE • CONTAINS Get Involved • Roadmap Being Discussed MapD (OSS) Working Group geo-wg@mapd.com • Beta Available Now Email Aaron - aaron@mapd.com
9. © MapD 2018 © MapD 2018 9 DEMO TIME #2
10. © MapD 2018 © MapD 2018 • mapd.com/demos Play with our demos • mapd.cloud Get a MapD instance in less than 60 seconds • mapd.com/platform/download-community/ Download the Community Edition • community.mapd.com Ask questions and share your experiences 10 Next Steps
11. © MapD 2018 Thank you! Questions? Aaron Williams VP of Global Community @_arw_ aaron@mapd.com /in/aaronwilliams/ /williamsaaron slides: https://speakerdeck.com/mapd/ Randy Zwitch Senior Developer Advocate @randyzwitch randy.zwitch@mapd.com /in/randyzwitch/ /randyzwitch Veda Shankar Senior Developer Advocate @veda_shankar veda.shankar@mapd.com /in/veda-shankar-6260a516/